Medicine & Health

424 verified cases, highest-rated first.

Industries: oncology 32, cardiology 27, medical imaging 20, drug discovery 18, digital pathology 14, dermatology 14, critical care 13, ophthalmology 9, oncology imaging 8, oncology pathology 8, infectious disease diagnostics 7, clinical trials 6

• Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction

  Cardiovascular disease, the leading global cause of death, needed better risk identification from accessible data.

  Medicine & Health · 2023 · ai assisted · 10/10 importance

• A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

  Cardiovascular disease is the leading cause of death worldwide (~17.9 million deaths/year), and early detection, prediction and monitoring are needed.

  Medicine & Health · 2023 · partially solved · 10/10 importance

• A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach

  Accurate early prediction of stroke is needed to enable timely treatment, but medical data is imbalanced.

  Medicine & Health · 2022 · partially solved · 10/10 importance

• Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning

  Cardiovascular disease often goes undiagnosed until serious events; non-invasive diagnosis from retinal images and DXA scans was lacking in Qatar.

  Medicine & Health · 2022 · partially solved · 10/10 importance

• Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential

  Predicting and managing COVID-19 disease severity was difficult, and mechanisms underlying severity were unclear.

  Medicine & Health · 2021 · partially solved · 10/10 importance

• Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization

  Detecting tuberculosis accurately and early from chest X-ray images, since late diagnosis can be life-threatening.

  Medicine & Health · 2020 · ai assisted · 10/10 importance

• Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model

  Forecasting the 60-day trajectory of total COVID-19 infections in the USA, Brazil, India and Russia to support pandemic planning.

  Medicine & Health · 2020 · ai assisted · 10/10 importance

• Deep learning approaches for breast cancer detection in histopathology images: A review

  Detecting and classifying breast cancer from histopathology images automatically, since manual analysis is essential but challenging.

  Medicine & Health · 2024 · partially solved · 9/10 importance

• SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting

  Forecasting the number of newly infected people one to four weeks ahead during the COVID-19 epidemic is hard because government policies change over time.

  Medicine & Health · 2022 · partially solved · 9/10 importance

• Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning

  Public health needs early prediction of new SARS-CoV-2 variant surges to prepare for immune escape and increased transmissibility.

  Medicine & Health · 2022 · partially solved · 9/10 importance

• A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

  During the COVID-19 pandemic, clinicians needed rapid automatic diagnosis from medical imaging such as CT and X-ray.

  Medicine & Health · 2021 · ai assisted · 9/10 importance

• Detection and analysis of COVID-19 in medical images using deep learning techniques

  Rapid, automatic detection of COVID-19 from X-ray and CT medical images.

  Medicine & Health · 2021 · ai assisted · 9/10 importance

• An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning

  Healthcare systems under pandemic pressure needed fast, early assessment of COVID-19 patient severity to allocate resources and reduce mortality.

  Medicine & Health · 2021 · ai assisted · 9/10 importance

• Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

  Fast, reliable diagnosis of COVID-19 from chest CT scans and X-ray images was needed during the pandemic.

  Medicine & Health · 2021 · ai assisted · 9/10 importance

• MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

  Sepsis kills about 5.4 million people worldwide each year and costs over 16 billion dollars in the USA alone, and early detection in hospitals is difficult.

  Medicine & Health · 2021 · ai assisted · 9/10 importance

• Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging

  COVID-19 testing demand overwhelmed capacity; RT-PCR is definitive but chest X-ray is fast and affordable, so AI detection of COVID-19 from CXR images was investigated.

  Medicine & Health · 2020 · partially solved · 9/10 importance

• Deep Learning Based Lung Cancer Detection and Classification

  Lung cancer must be detected early from CT scans, since late diagnosis sharply reduces survival rates.

  Medicine & Health · 2020 · partially solved · 9/10 importance

• Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals

  Heartbeat events need accurate classification from single- or multi-lead ECG signals to support cardiovascular disease screening and diagnosis.

  Medicine & Health · 2020 · ai assisted · 9/10 importance

• Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

  Predicting cardiovascular disease from clinical data to support earlier decisions and interventions.

  Medicine & Health · 2019 · ai assisted · 9/10 importance

• A deep learning framework for automated early diagnosis and classification of skin cancer lesions in dermoscopy images

  Manual diagnosis of skin cancer from dermoscopy images is challenging due to subtle visual differences between benign and malignant lesions.

  Medicine & Health · 2025 · partially solved · 8/10 importance

• Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models

  Predicting diabetes and identifying causal risk factors from patient health data, to understand contributors to a growing global disease.

  Medicine & Health · 2025 · ai assisted · 8/10 importance

• Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification

  Early skin cancer detection requires accurate segmentation and classification of skin lesion images, where misdiagnosis can be fatal.

  Medicine & Health · 2025 · ai assisted · 8/10 importance

• An explainable machine learning approach for Alzheimer’s disease classification

  Early Alzheimer's diagnosis is difficult because subtle biomarker changes are overlooked and data is high-dimensional with limited samples.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• Deep-learning-enabled antibiotic discovery through molecular de-extinction

  Antibiotic resistance requires new antimicrobial candidates; the work mines proteomes of extinct organisms for antibiotic peptides.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• Zero-Shot Clinical Trial Patient Matching with LLMs

  Manually matching patients to clinical trial eligibility criteria takes up to 1 hour per patient, slowing drug development.

  Medicine & Health · 2024 · partially solved · 8/10 importance

• Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery

  Drug discovery is slow and costly, and diseases like Marburg virus lack treatments, so faster identification of candidate compounds is needed.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

  Healthcare-associated infections are the most common adverse events in healthcare, and conventional surveillance is costly and labor intensive.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review

  Fast, accurate clinical diagnosis of cardiovascular diseases from ECG traces, the most widely used tool in cardiology.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images

  Diagnosing multiple lung diseases (fibrosis, tuberculosis, pneumonia, COVID-19) from chest X-ray images quickly and accurately is difficult and requires expert radiologists.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis

  Lung cancer diagnosis is often delayed and relies on pathologist review of histopathology slides, contributing to cancer-related deaths.

  Medicine & Health · 2024 · ai assisted · 8/10 importance

• Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

  Most eligible people are not screened for lung cancer, and tools for personalized future risk from a single LDCT were lacking.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• A foundation model for generalizable disease detection from retinal images

  AI models for retinal image analysis typically require large labeled datasets and are task-specific, limiting generalizable detection of eye and systemic diseases.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Large-scale pancreatic cancer detection via non-contrast CT and deep learning

  Pancreatic ductal adenocarcinoma is typically detected late and inoperable, and identifying it on non-contrast CT was long considered impossible.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Accelerating drug target inhibitor discovery with a deep generative foundation model

  Discovering small-molecule inhibitors for emerging drug targets when target structure or known active molecules are unavailable.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods

  Drug discovery and repositioning demand high resource investment and are time-consuming for the pharmaceutical industry.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Discovery of senolytics using machine learning

  Only a few senolytics (drugs eliminating senescent cells) are known, and experimental screening for new ones is costly.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images

  Tuberculosis is often misdiagnosed and untreated, especially in low-resource areas lacking radiologists to read chest X-rays.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review

  Detecting sepsis early enough to intervene, since it rapidly progresses to life-threatening forms; ML/DL on electronic health records was surveyed as an approach.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

  It was unclear whether deep learning predictions of genetic alterations from tumor histopathology slides generalize to external datasets across cancer types.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis

  Sepsis in ICU patients is often recognized too late, and early recognition is crucial for effective treatment. The study systematically evaluated how well machine learning models predict sepsis onset.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Cervical cancer survival prediction by machine learning algorithms: a systematic review

  Predicting survival time for cervical cancer patients is difficult; machine learning has been proposed to improve these predictions.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification

  Mpox symptoms resemble chickenpox and measles, causing possible misdiagnosis, and a rapid, automated early diagnosis method is needed.

  Medicine & Health · 2023 · partially solved · 8/10 importance

• Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**

  Antimicrobial resistance and a stalled antibiotic pipeline make new antimicrobial compounds urgently needed; metal complexes are promising but understudied.

  Medicine & Health · 2023 · solved · 8/10 importance

• A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images

  Breast cancer, common and dangerous in women and men, needs accurate detection from histopathology images containing phenotypic data.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Classification and deep-learning–based prediction of Alzheimer disease subtypes by using genomic data

  Late-onset Alzheimer's disease is heterogeneous, and genetic risk factors for its subtypes were previously unidentified, limiting subtype-specific prediction.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

  Drug discovery is long and costly, and deep learning results are often not confirmed experimentally before impacting development.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis

  Deep networks for diagnosing colon and lung cancers from histopathology images need improved performance.

  Medicine & Health · 2023 · ai assisted · 8/10 importance

• Stroke Risk Prediction with Machine Learning Techniques

  Predicting long-term risk of stroke occurrence early enough to enable prevention and healthier living.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Risk prediction of cardiovascular disease using machine learning classifiers

  Early, automatic detection of cardiovascular disease to prevent death or physical paralysis.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Detection of COVID-19 Based on Chest X-rays Using Deep Learning

  Fast, reliable early detection of COVID-19 from chest X-ray images to support diagnosis and isolation decisions.

  Medicine & Health · 2022 · partially solved · 8/10 importance

• Deep Learning Applications in Computed Tomography Images for Pulmonary Nodule Detection and Diagnosis: A Review

  Lung cancer has one of the highest mortality rates of all cancers, and early detection of pulmonary nodules in CT scans is crucial for survival. The paper reviews computer-aided diagnosis systems that detect and classify such nodules.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review

  Early detection and localization of myocardial infarction via ECG is life-saving, but population-wide ECG screening is too labor-intensive without automation.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

  Malaria diagnosis by manual microscopy is slow and requires a well-trained microscopist, limiting diagnosis in resource-poor settings.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method

  Detecting lymph node metastases from whole-slide pathology images, which is time-consuming with current methods.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learning, and Unsupervised Clustering of Gene Expression

  Finding new uses for existing drugs is slow and costly; the study aimed to identify repurposing candidates by linking diseases through shared gene-expression patterns.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets

  Sepsis is hard to identify early, and late diagnosis increases mortality, so better early screening tools are needed.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Machine Learning Models for Cardiovascular Disease Events Prediction

  Cardiovascular diseases cause high mortality worldwide, and early prediction of CVD-related death within ten years could enable prevention.

  Medicine & Health · 2022 · ai assisted · 8/10 importance

• Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare

  Sepsis is a leading cause of hospital death and hard to predict early because its signs resemble less critical conditions.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Machine Learning in Action: Stroke Diagnosis and Outcome Prediction

  Stroke requires rapid diagnosis and triage, while accurate outcome prediction depends on many patient-specific and clinical factors.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

  Rapid, reliable differentiation of COVID-19 from non-COVID-19 patients using bedside chest x-rays was needed during the Lombardy outbreak.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction

  Diagnosing Alzheimer's disease and determining its stage early from MRI brain scans, so patients can begin preventive care and treatment sooner.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• A deep learning algorithm for detecting acute myocardial infarction

  Acute myocardial infarction is delayed or misdiagnosed in daily practice, and 12-lead ECG interpretation needs strengthening.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Pre-existing and machine learning-based models for cardiovascular risk prediction

  Cardiovascular risk prediction models needed assessment and improvement using large, complex healthcare data for primary prevention.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Early Prediction of Sepsis Based on Machine Learning Algorithm

  Sepsis, an infection-driven organ failure with extremely high mortality, needs to be predicted early to improve outcomes.

  Medicine & Health · 2021 · partially solved · 8/10 importance

• Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

  Large-scale next-generation sequencing data in cancer research creates big-data challenges, requiring sophisticated analysis to characterize tumors and tumor subtypes.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

  Predicting dengue fever epidemic years in Brazilian cities months in advance is hard due to complex weather, mosquito and human immunity dynamics.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis

  Early recognition of sepsis in emergency department patients, which is difficult but critical for timely antibiotic treatment.

  Medicine & Health · 2021 · ai assisted · 8/10 importance

• Faculty Opinions recommendation of A deep learning approach to antibiotic discovery.

  Rapidly emerging antibiotic-resistant bacteria create an urgent need for new antibiotics with novel structures.

  Medicine & Health · 2020 · partially solved · 8/10 importance

• Machine learning prediction in cardiovascular diseases: a meta-analysis

  Accurately predicting cardiovascular diseases including coronary artery disease, heart failure, stroke, and cardiac arrhythmias.

  Medicine & Health · 2020 · partially solved · 8/10 importance

• Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

  Detecting and classifying pneumonia from chest X-ray images with a model that meets all performance metrics.

  Medicine & Health · 2020 · partially solved · 8/10 importance

• CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV

  TB in HIV-positive patients often goes undiagnosed, and chest x-ray diagnosis is difficult due to atypical presentation and radiologist shortages.

  Medicine & Health · 2020 · ai assisted · 8/10 importance

• An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images

  Screening and diagnosing COVID-19 patients quickly from chest X-rays to reduce the burden on medical experts.

  Medicine & Health · 2020 · ai assisted · 8/10 importance

• Deep Learning-driven research for drug discovery: Tackling Malaria

  Discovering new antimalarial drug candidates is slow and expensive, and parasites constantly develop resistance to current drugs.

  Medicine & Health · 2020 · ai assisted · 8/10 importance

• Matrix Factorization-based Technique for Drug Repurposing Predictions

  Developing new drugs is hugely costly and slow, with about 85% of proposed molecules failing the first three FDA approval phases, so finding new indications for already approved drugs is needed.

  Medicine & Health · 2020 · ai assisted · 8/10 importance

• Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria

  Many potential antibiotics cannot enter Gram-negative bacteria such as multidrug-resistant Pseudomonas aeruginosa, limiting treatment options.

  Medicine & Health · 2020 · ai assisted · 8/10 importance

• A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

  Detecting lung nodules and diagnosing malignancy accurately from low-dose CT screening scans.

  Medicine & Health · 2019 · ai assisted · 8/10 importance

• Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis

  Predicting all-cause mortality and major cardiovascular events (heart failure, coronary syndrome, stroke, hemorrhage) in patients, and comparing machine learning with conventional logistic regression risk models.

  Medicine & Health · 2019 · ai assisted · 8/10 importance

• Predicting High-Risk Prostate Cancer Using Machine Learning Methods

  PSA-based prostate cancer screening produces both false positives and false negatives, making it hard to identify which patients have high-risk prostate cancer.

  Medicine & Health · 2019 · partially solved · 8/10 importance

• Identification of Novel Antibacterials Using Machine Learning Techniques

  Pharmaceutical companies avoid developing novel antibacterials despite urgent need for new antibiotics against resistant bacterial strains.

  Medicine & Health · 2019 · ai assisted · 8/10 importance

• A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18 F-FDG PET of the Brain

  Predicting the final diagnosis of Alzheimer disease, mild cognitive impairment, or neither from brain 18F-FDG PET images is difficult for radiologic readers.

  Medicine & Health · 2018 · ai assisted · 8/10 importance

• Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA

  The standard ACC/AHA CVD risk calculator underestimates risk in high-risk individuals and overestimates it in low-risk ones, causing missed therapy or unnecessary statins.

  Medicine & Health · 2018 · partially solved · 8/10 importance

• Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach

  Rural areas lack access to CT screening, so lung cancer classification from widely available chest X-rays was needed.

  Medicine & Health · 2018 · ai assisted · 8/10 importance

• Prediction of survival and metastasis in breast cancer patients using machine learning classifiers

  Predicting survival and metastasis in breast cancer patients accurately enough to improve early diagnosis and treatment decisions.

  Medicine & Health · 2018 · ai assisted · 8/10 importance

• Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures

  Malaria resistance is developing rapidly, and exhaustively screening large drug spaces for synergistic antimalarial combinations experimentally is infeasible.

  Medicine & Health · 2018 · ai assisted · 8/10 importance

• Cardiovascular Event Prediction by Machine Learning

  Standard cardiovascular risk scores may miss complex patterns in large, heterogeneous patient data, limiting event prediction accuracy.

  Medicine & Health · 2017 · ai assisted · 8/10 importance

• Computer Vision Malaria Diagnostic Systems—Progress and Prospects

  Malaria diagnosis by microscopy and rapid tests can be slow, inconsistent and inaccurate, contributing to fatalities and drug overuse.

  Medicine & Health · 2017 · ai assisted · 8/10 importance

• The Large Scale Machine Learning in an Artificial Society: Prediction of the Ebola Outbreak in Beijing

  Governments struggle to predict possible Ebola epidemic scenarios in practice when drawing up emergency plans.

  Medicine & Health · 2015 · ai assisted · 8/10 importance

• De novo design of peptide binders to conformationally diverse targets with contrastive language modeling

  Designing molecules that bind to 'undruggable' disease proteins is a major challenge in drug discovery. PepPrCLIP was created to design short target-binding peptides using only the target protein's amino acid sequence.

  Medicine & Health · 2025 · partially solved · 7/10 importance

• Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson’s disease

  There was no consensus on which gut microbiome features are associated with Parkinson's disease or how well they can diagnose it across multiple studies.

  Medicine & Health · 2025 · partially solved · 7/10 importance

• Peptide-based drug design using generative AI

  Designing peptide-based drugs is slow and hindered by poor solubility, immunogenicity, toxicity and bioavailability.

  Medicine & Health · 2025 · ai assisted · 7/10 importance

• A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images

  Diabetic retinopathy needs to be detected early and its progression monitored from retinal fundus images to guide treatment.

  Medicine & Health · 2025 · ai assisted · 7/10 importance

• Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

  Screening for diabetic kidney disease and distinguishing isolated diabetic nephropathy from non-diabetic kidney disease are major access-limited challenges in diabetes care.

  Medicine & Health · 2025 · partially solved · 7/10 importance

• A scoping review of artificial intelligence applications in clinical trial risk assessment

  Assessing safety, efficacy, and operational risks in clinical trials is difficult; AI is increasingly applied to improve it.

  Medicine & Health · 2025 · partially solved · 7/10 importance

• Deep learning quantifies pathologists’ visual patterns for whole slide image diagnosis

  Training deep learning diagnostic models on whole slide images requires massive pixelwise annotation time from pathologists, limiting dataset size and diagnostic precision.

  Medicine & Health · 2025 · solved · 7/10 importance

• Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Comprehensive Review

  A literature review examining how AI can improve analysis, image quality, processing speed and diagnostic accuracy across noninvasive cardiac imaging modalities (echocardiography, MRI, CT, nuclear).

  Medicine & Health · 2025 · ai assisted · 7/10 importance

• Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review

  Sepsis is life-threatening and early detection is difficult; delays significantly worsen patient outcomes.

  Medicine & Health · 2025 · ai assisted · 7/10 importance

• Deep learning for electrocardiogram interpretation: Bench to bedside

  Automating ECG analysis to detect and predict rhythm, conduction, ischemic and structural heart diseases, where clinical integration of algorithms remains limited.

  Medicine & Health · 2025 · partially solved · 7/10 importance

• A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

  Idiopathic pulmonary fibrosis is an aggressive, high-mortality lung disease where putative drug targets have failed to translate into effective clinical therapies.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Almanac — Retrieval-Augmented Language Models for Clinical Medicine

  Large language models tend to generate incorrect or harmful statements, limiting their adoption for clinical medical guideline and treatment recommendations.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Matching patients to clinical trials with large language models

  Patient recruitment for clinical trials is challenging; matching patients to eligible trials manually does not scale.

  Medicine & Health · 2024 · solved · 7/10 importance

• A foundation model for clinician-centered drug repurposing

  Drug repurposing AI models focus narrowly on diseases that already have drugs, leaving diseases with limited or no treatment options unaddressed.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Prospective de novo drug design with deep interactome learning

  Designing new drug-like molecules from scratch with specific bioactivity, synthesizability and structural novelty is a core challenge in drug discovery.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• De novo generation of SARS-CoV-2 antibody CDRH3 with a pre-trained generative large language model

  Discovering antigen-specific antibodies relies on isolating them from serum, a resource-intensive and time-consuming process.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

  Detecting congenital heart disease in children early using low-cost, non-invasive pediatric ECG instead of more invasive or expensive methods.

  Medicine & Health · 2024 · solved · 7/10 importance

• A reproducible ensemble machine learning approach to forecast dengue outbreaks

  Dengue outbreaks are hard to predict countrywide because incidence varies across regions and time.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Early detection of dementia through retinal imaging and trustworthy AI

  Alzheimer's disease lacks a simple, affordable early detection method, leaving early-onset AD and mild cognitive impairment hard to identify.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Morphological profiling for drug discovery in the era of deep learning

  Analyzing large-scale high-content cell images to understand compound mechanism-of-action, drug repurposing, and morphodynamics under perturbation.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer

  Surgeons need to quickly distinguish cancerous breast tissue from healthy tissue during surgery to ensure complete tumor removal.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study

  Sepsis in ICU patients is often detected too late, so clinicians need tools that predict its onset hours in advance.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Predicting malaria outbreak in The Gambia using machine learning techniques

  Malaria outbreaks in the districts of The Gambia are hard to anticipate, posing a growing threat to public health and economic growth.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Antibiotic discovery with artificial intelligence for the treatment of Acinetobacter baumannii infections

  Multidrug-resistant Acinetobacter baumannii infections are difficult to treat, and new antibiotic candidates are needed against targets such as outer membrane protein W (OmpW).

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Deep learning-based prediction of in-hospital mortality for sepsis

  Existing clinical models for assessing mortality risk in sepsis patients rely on limited conventional indicators, leading to inaccurate risk assessment in the ICU.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach

  Identifying pathogenic variants among the vast majority of human nucleotide variation remains a challenge.

  Medicine & Health · 2024 · partially solved · 7/10 importance

• Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique

  Alzheimer's disease has no cure, so detecting it early from MRI scans and distinguishing its stages is critical but difficult.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Machine learning for early dynamic prediction of functional outcome after stroke

  Predicting functional outcome after stroke is complicated by fluctuations in the first days, yet is needed for treatment planning and resource allocation.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study

  Stroke is a major public health concern, and predicting who will have one and identifying key risk factors from cohort data remains challenging.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models

  Malaria and typhoid are hard to diagnose accurately due to similar symptoms, and ML predictions are opaque to healthcare workers.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review

  Malaria diagnosis by light microscopy is slow and requires rare expert pathologists, delaying timely treatment.

  Medicine & Health · 2024 · ai assisted · 7/10 importance

• Accurate proteome-wide missense variant effect prediction with AlphaMissense

  The vast majority of human missense genetic variants have unknown clinical significance, hindering genetic disease diagnosis.

  Medicine & Health · 2023 · partially solved · 7/10 importance

• Large language models encode clinical knowledge

  Reliably assessing whether large language models possess real clinical knowledge, beyond limited automated benchmarks.

  Medicine & Health · 2023 · partially solved · 7/10 importance

• Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

  Standard filtered back projection CT reconstruction suffers from noise and artifacts at lower doses, while model-based iterative reconstruction is slow and yields unnatural image appearance.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Genome-wide prediction of disease variant effects with a deep protein language model

  Existing deep-learning methods could not predict the effects of all possible human coding variants, limiting genetic diagnosis and interpretation.

  Medicine & Health · 2023 · solved · 7/10 importance

• Machine Learning and Prediction of Infectious Diseases: A Systematic Review

  Predicting infectious disease outbreaks early enough to act is difficult; this systematic review assessed whether machine learning can do it.

  Medicine & Health · 2023 · partially solved · 7/10 importance

• Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

  Manual detection of retinal diseases such as glaucoma, diabetic retinopathy and AMD is time-consuming, tedious and lacks repeatability.

  Medicine & Health · 2023 · partially solved · 7/10 importance

• A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

  Early diagnosis of Alzheimer's disease is difficult, and its growing prevalence imposes major costs on individuals and society.

  Medicine & Health · 2023 · partially solved · 7/10 importance

• Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

  Jointly detecting pneumonia, COVID-19 and tuberculosis from chest X-rays, since patients may have more than one lung disease at once.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Applications of artificial intelligence in clinical laboratory genomics

  Genomic data volumes are exceeding human capacity to rapidly and reproducibly analyze complex molecular data for timely diagnosis and management of genomic disorders.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• A Survey on Deep-Learning-Based Diabetic Retinopathy Classification

  Diabetic retinopathy damages the eyes of long-term diabetes patients, and early automatic detection is needed to avoid vision loss.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood Supply Chain Using Machine Learning

  Blood supply chains suffer from uncertain demand, causing wastage and shortages when inventory is insufficient.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project

  Patients with diabetes are at high risk of cardiovascular events, and predicting which patients will suffer events is difficult.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

  Dengue virus has no approved antivirals, so computational methods are needed to predict small-molecule inhibitors and repurposed drug candidates.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• Machine learning applications on neonatal sepsis treatment: a scoping review

  Neonatal sepsis has non-specific signs, treatment is labour-intensive, and antimicrobial resistance complicates empiric antibiotic choice.

  Medicine & Health · 2023 · ai assisted · 7/10 importance

• A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study

  No simple screening model exists for Alzheimer's disease; diagnosis relies on expensive, sometimes invasive tests unavailable outside specialised settings.

  Medicine & Health · 2022 · partially solved · 7/10 importance

• AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens

  Antibiotic resistance creates urgent need for new antimicrobial agents, but lab screening of candidate peptides is costly.

  Medicine & Health · 2022 · partially solved · 7/10 importance

• MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

  Existing computational tools struggle to distinguish rare pathogenic from rare benign missense SNVs and non-frameshift InDels, hindering genetic analysis.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images

  Classifying lung diseases such as pneumonia, pneumothorax and tuberculosis from chest X-rays is slow and error-prone, limiting computer-aided diagnosis.

  Medicine & Health · 2022 · partially solved · 7/10 importance

• Alzheimer’s disease diagnosis and classification using deep learning techniques

  Manual diagnosis of Alzheimer's disease is error-prone and time-consuming, and early accurate detection is needed.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images

  Pathologist grading of breast cancer shows significant inter-observer variation, and prior computer methods only work on selected tissue areas or need labor-intensive annotations.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity

  Predicting whether missense gene variants are pathogenic is difficult, hindering diagnosis of hereditary disease and cancer.

  Medicine & Health · 2022 · partially solved · 7/10 importance

• Deep Learning for Bone Mineral Density and T-Score Prediction from Chest X-rays: A Multicenter Study

  Osteoporosis diagnosis and treatment are inadequate worldwide; the study predicts bone mineral density and T-score from routine chest X-rays.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics

  Tuberculosis meningitis is notoriously hard to diagnose in sub-Saharan Africa, and causes of meningitis there are poorly understood.

  Medicine & Health · 2022 · partially solved · 7/10 importance

• Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy

  Current guideline ECG criteria poorly predict which patients will benefit from cardiac resynchronization therapy (CRT).

  Medicine & Health · 2022 · partially solved · 7/10 importance

• Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study

  Shortage of licensed ABA practitioners and subjective, non-data-driven planning limits timely autism treatment.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Prediction of incident cardiovascular events using machine learning and CMR radiomics

  Predicting whether patients will develop atrial fibrillation, heart failure, myocardial infarction, or stroke before symptoms occur.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• A machine learning model trained on a high-throughput antibacterial screen increases the hit rate of drug discovery

  Screening small molecule libraries for novel antibacterial compounds has a very low success rate (0.87% hit rate against antibiotic-resistant Burkholderia cenocepacia).

  Medicine & Health · 2022 · partially solved · 7/10 importance

• Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension

  Elevated pulmonary artery pressure in pulmonary hypertension requires early detection from ECG to enable prompt diagnosis and treatment.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer

  Surgical oncologists need reliable individual long-term survival estimates for patients after resection of gastric or distal esophageal cancer.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Deep learning-based framework for slide-based histopathological image analysis

  Whole-slide histopathology image analysis lacked a general slide-based framework, with existing methods being task-dependent.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

  Detection, segmentation and classification of breast cancer in histopathology images is difficult; the study reviews deep learning approaches to it.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning

  Intracranial hemorrhage is dangerous with high mortality without rapid diagnosis, and CT images take long and require much expertise to analyze.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Performance effectiveness of vital parameter combinations for early warning of sepsis—an exhaustive study using machine learning

  Detecting sepsis early using only noninvasive bedside vital signs so clinicians can intervene sooner.

  Medicine & Health · 2022 · ai assisted · 7/10 importance

• Federated learning for predicting clinical outcomes in patients with COVID-19

  Predicting future oxygen needs of symptomatic COVID-19 patients using data from many hospitals without sharing patient data.

  Medicine & Health · 2021 · solved · 7/10 importance

• Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

  De novo drug design, generating novel molecular structures from atomic building blocks, is difficult and costly with conventional structure- or ligand-based methods.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

  Genetic variants affecting splicing are hard to identify beyond donor/acceptor dinucleotides, underpinning much genetic disease.

  Medicine & Health · 2021 · partially solved · 7/10 importance

• Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

  Detecting multiple retinal fundus diseases and conditions early enough to prevent irreversible visual impairment, previously addressed only one disease at a time.

  Medicine & Health · 2021 · solved · 7/10 importance

• Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

  Early detection of variable brain diseases (Alzheimer's, brain tumor, epilepsy, Parkinson's) remains an open diagnostic challenge.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

  Alzheimer's disease drug trials are slow, expensive and largely failing, so existing approved drugs need to be screened for reuse.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

  Accurate detection and classification of breast cancer from histopathology images is critical but difficult due to the complexity of breast tissues.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• A generalized deep learning framework for whole-slide image segmentation and analysis

  Analyzing whole-slide histopathology images for cancer diagnosis is time-consuming because of their large size, heterogeneity, and rising case numbers.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• MVP predicts the pathogenicity of missense variants by deep learning

  Accurately predicting whether missense genetic variants are pathogenic is critical for genetic studies and clinical diagnosis, and prior methods had limited performance.

  Medicine & Health · 2021 · partially solved · 7/10 importance

• Robust whole slide image analysis for cervical cancer screening using deep learning

  Current algorithms perform poorly on whole slide image analysis for cervical cancer screening, failing to generalize across staining and imaging conditions.

  Medicine & Health · 2021 · solved · 7/10 importance

• A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans

  Diagnosing acute intracranial hemorrhage on non-contrast head CT varies greatly among radiologists due to subtle findings and heavy time pressure.

  Medicine & Health · 2021 · partially solved · 7/10 importance

• Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram

  Growing ECG exam volumes and a shortage of cardiologists make automatic, accurate arrhythmia diagnosis from 12-lead ECGs necessary.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques

  Predicting dengue fever outbreaks in five high-incidence districts of Selangor, Malaysia using climate variables.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

  Manual microscopic examination of blood smears, the gold standard for malaria diagnosis, depends on skilled microscopists who are scarce in remote developing regions.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Random forest-based prediction of stroke outcome

  Clinicians lack tools to predict which stroke patients will die or suffer morbidity three months after admission.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram

  Rapidly assessing left and right ventricular (heart pump) function from a routine ECG was not possible; no deep learning tools existed for right ventricular function and left ventricular tools covered only very low function.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

  Identifying driver genetic mutations in breast cancer normally requires costly molecular profiling, so predicting them directly from routine whole-slide images was investigated.

  Medicine & Health · 2021 · partially solved · 7/10 importance

• DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration

  Predicting how cancer cell lines respond to anticancer drugs is bottlenecked by the lack of effective methods for integrating multimodal, multisource omics data.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction

  Detecting left ventricular systolic dysfunction (LVSD), which raises morbidity and mortality even when asymptomatic, using ECG rather than echocardiography.

  Medicine & Health · 2021 · solved · 7/10 importance

• Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

  Clinicians find it difficult to predict early outcomes (mortality, severity, length of ICU stay) for sepsis patients in intensive care.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

  Gastric cancer responds heterogeneously to immunotherapy, and identifying immune subtypes could improve therapeutic strategies.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

  Doctors lack a reliable way to select the best osteoporosis treatment regimen for individual patients to improve bone mineral density and prevent fractures.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Diagnostic assessment of deep learning for melanocytic lesions using whole-slide pathological images

  Discriminating melanoma from nevus on whole-slide pathological images is difficult and depends on expert dermatopathologists.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms

  Acute myocardial infarction (AMI) needs rapid, accurate diagnosis from 12-lead ECGs so the culprit lesion can be treated early.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models

  Preventable or undiagnosed visual impairment and blindness affect billions worldwide, needing automated multi-disease detection in retinal imaging.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Predicting malaria epidemics in Burkina Faso with machine learning

  Malaria outbreaks in Burkina Faso previously required complicated numerical transmission simulations, preventing simple data-driven early warning.

  Medicine & Health · 2021 · partially solved · 7/10 importance

• Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram

  Heart failure with preserved ejection fraction (HFpEF) lacks typical ECG features and currently requires clinical, invasive, and laboratory exams to diagnose. The study tested whether AI could detect HFpEF from a baseline ECG.

  Medicine & Health · 2021 · ai assisted · 7/10 importance

• Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

  Predicting 30-day mortality in ICU sepsis-3 patients (MIMIC-III) early enough to guide treatment and improve survival.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning

  Eight types of skin lesions look highly similar, causing inaccurate diagnosis and missed early melanoma detection.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Deep learning-based survival prediction for multiple cancer types using histopathology images

  Predicting disease-specific survival risk for cancer patients at diagnosis from histopathology images, where existing staging and clinical variables leave room for better risk stratification.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning

  Grading hepatocellular carcinoma requires visual inspection by experienced pathologists, and mutated gene status normally requires genomic testing.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

  Detecting malignant lung nodules in CT scans is hard and time-consuming for radiologists, motivating computer-aided diagnosis systems.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Deep Learning for Hemorrhagic Lesion Detection and Segmentation on Brain CT Images

  Locating hemorrhagic strokes in brain CT images is challenging and time-consuming even for experienced radiologists due to high variability in location, contrast, and shape.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019

  Automatically segmenting lung cancer tissue pixel-wise in whole-slide histopathology images to support diagnosis.

  Medicine & Health · 2020 · partially solved · 7/10 importance

• Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer

  State-of-the-art diagnostics lack automated methods to characterize and quantify tumor heterogeneity in a spatial context from pathology whole-slide images.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center

  Fewer than 5% of cancer patients enroll in clinical trials and 1 in 5 trials stop for poor accrual, partly because manual eligibility screening from unstructured records is slow.

  Medicine & Health · 2020 · partially solved · 7/10 importance

• Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm

  Detecting intracranial haemorrhage and classifying its subtypes on CT images is difficult and needs accurate automated methods.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system

  Manual ECG interpretation is error-prone; automated detection and classification of cardiovascular disease from ECG signals was needed.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Knowledge-driven drug repurposing using a comprehensive drug knowledge graph

  New drug discovery and development is hugely expensive, so finding new uses for existing drugs is needed.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Machine learning-guided discovery and design of non-hemolytic peptides

  Antimicrobial peptides are promising against antibiotic-resistant infections but their hemolytic toxicity blocks clinical use, and reliable computational prediction is hard.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Machine learning for predicting cardiac events: what does the future hold?

  Traditional statistical models cannot predict individual-level risk of cardiovascular events such as heart failure and arrhythmias.

  Medicine & Health · 2020 · partially solved · 7/10 importance

• Intracranial Hemorrhage Detection in CT Scans using Deep Learning

  Patient mortality in intracranial hemorrhage depends on prompt diagnosis from radiologist assessment of CT scans; a deep learning model was built to accelerate identification.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features

  Detecting skin diseases and skin cancer from dermoscopy color images to aid dermatologists in diagnosis.

  Medicine & Health · 2020 · ai assisted · 7/10 importance

• Faculty Opinions recommendation of A clinically applicable approach to continuous prediction of future acute kidney injury.

  About 11% of hospital deaths follow failure to promptly recognize deteriorating patients; continuous, accurate, individual-level risk prediction is needed, using acute kidney injury as an exemplar.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Breast cancer outcome prediction with tumour tissue images and machine learning

  Predicting breast cancer patient outcomes from tumour tissue images alone, rather than relying solely on human expert assessment.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Machine learning-powered antibiotics phenotypic drug discovery

  Identifying novel antibiotics and determining compound mode of action remains a major drug-discovery challenge, with weak hits often underexploited.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Pediatric Severe Sepsis Prediction Using Machine Learning

  Severe sepsis in hospitalized children is hard to detect early enough to optimize effective treatment.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data

  Predicting breast cancer survival is needed to help select the best treatment protocols for patients.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Machine learning to predict cardiovascular risk

  Accurately estimating a patient's cardiovascular event risk is difficult, and traditional scales may be outperformed by data-driven methods.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach

  Predicting colon cancer TNM stage and five-year disease-free survival from histopathology parameters is difficult.

  Medicine & Health · 2019 · partially solved · 7/10 importance

• On classifying sepsis heterogeneity in the ICU: insight using machine learning

  Current ML models predicting sepsis from electronic health records ignore the condition's heterogeneity, limiting risk recognition in the ICU.

  Medicine & Health · 2019 · ai assisted · 7/10 importance

• Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning

  Predicting mortality of suspected infected patients in hospital emergency departments, where sepsis is the most common cause of death.

  Medicine & Health · 2019 · partially solved · 7/10 importance

• Lung Cancer Detection and Classification Using Deep Learning

  Detecting lung nodules in CT scans and classifying their malignancy level at an early stage is difficult and important for diagnosis.

  Medicine & Health · 2018 · ai assisted · 7/10 importance

• Classification of retinal images with deep learning for early detection of diabetic retinopathy disease

  Diabetic retinopathy, a leading cause of blindness in people with diabetes, needs to be detected early from retinal images to prevent vision loss.

  Medicine & Health · 2018 · ai assisted · 7/10 importance

• Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

  Detecting referable and vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration from retinal images in multiethnic populations with diabetes.

  Medicine & Health · 2017 · solved · 7/10 importance

• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

  Detecting pneumonia accurately from chest X-rays, a task normally requiring expert radiologist interpretation.

  Medicine & Health · 2017 · partially solved · 7/10 importance

• Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent

  Manually identifying the presence and extent of invasive breast cancer in whole-slide pathology images is tedious and subject to inter- and intra-reader variability.

  Medicine & Health · 2017 · ai assisted · 7/10 importance

• Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning

  Automated detection of diabetic retinopathy from fundus images needed improvement; a deep-learning-enhanced algorithm was benchmarked against a consensus reference standard from three US board-certified retinal specialists.

  Medicine & Health · 2016 · solved · 7/10 importance

• Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach

  Existing clinical decision rules for predicting in-hospital mortality in emergency department sepsis patients use few variables, generalize poorly, and cannot be easily updated.

  Medicine & Health · 2015 · ai assisted · 7/10 importance

• Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore

  Singapore, with endemic dengue and no vaccine on the market, needed forecasts of epidemic evolution up to three months ahead to support public health response.

  Medicine & Health · 2015 · ai assisted · 7/10 importance

• Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

  Predicting how sensitive specific cancer cell lines are to drug treatment, a step toward personalised cancer therapy.

  Medicine & Health · 2013 · partially solved · 7/10 importance

• Discovery of EP4 antagonists with image-guided explainable deep learning workflow

  Manual compound library design and slow wet-lab screening limit efficient discovery of novel EP4-targeted cancer immunotherapy drugs.

  Medicine & Health · 2025 · ai assisted · 6/10 importance

• Current applications and challenges in large language models for patient care: a systematic review

  Information on how large language models are used and limited in patient care was unsystematized.

  Medicine & Health · 2025 · ai assisted · 6/10 importance

• Best practices for developing microbiome-based disease diagnostic classifiers through machine learning

  Microbiome-based ML disease diagnostic models lack a standardized workflow; tool and parameter choices hurt performance and generalizability.

  Medicine & Health · 2025 · partially solved · 6/10 importance

• Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations

  Physicians spend substantial time and cognitive effort on medical documentation during patient encounters.

  Medicine & Health · 2025 · ai assisted · 6/10 importance

• Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Comparative Analysis of Large Language Model Tools and Mental Health Professionals

  Whether large language models can accurately assess mental health disorders and recommend treatments compared to mental health professionals was underexplored.

  Medicine & Health · 2025 · partially solved · 6/10 importance

• A machine learning approach for multimodal data fusion for survival prediction in cancer patients

  Cancer patient survival prediction lacked a comprehensive machine-learning pipeline for comparing multimodal data fusion methods.

  Medicine & Health · 2025 · partially solved · 6/10 importance

• Classifying and diagnosing Alzheimer’s disease with deep learning using 6735 brain MRI images

  Diagnosing and classifying Alzheimer's disease from brain MRI scans, where traditional methods are slow and less accurate.

  Medicine & Health · 2025 · ai assisted · 6/10 importance

• Implementation of large language models in electronic health records

  Electronic health records have increased clinicians' documentation workload, and real-world LLM deployments in live hospital systems remain limited.

  Medicine & Health · 2025 · ai assisted · 6/10 importance

• Drug-drug interactions prediction based on deep learning and knowledge graph: A review

  Detecting drug-drug interactions through biological or pharmacological analysis is time-consuming and expensive, leaving patients at risk of adverse events.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRISPR ribonucleoprotein delivery

  Designing polymer carriers that efficiently deliver pDNA and CRISPR-Cas9 ribonucleoprotein into cells is hard because the structure-property relationships in combinatorial polymer libraries are complex and poorly understood.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Deep learning for accelerated and robust MRI reconstruction

  MRI scans are slow and traditional reconstruction faces image quality, acceleration and data-related challenges.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review

  Recruiting and retaining participants in clinical trials is difficult and often limits research progress.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation

  Early prediction of 28-day death risk in ICU patients with sepsis-induced coagulopathy lacked an interpretable, generalizable model.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology

  Detecting microsatellite instability and POLE mutations in colorectal cancer from histopathology slides to identify immunotherapy candidates.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• Enhancing missense variant pathogenicity prediction with protein language models using VariPred

  Predicting whether rare and novel genetic missense variants are pathogenic is difficult, limiting clinical interpretation of sequencing results.

  Medicine & Health · 2024 · partially solved · 6/10 importance

• Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies

  Deep learning pneumonia diagnosis on chest X-rays had mainly been validated against radiologists, whose own diagnoses can be inaccurate, so accuracy was tested against a robust multimodal expert reference.

  Medicine & Health · 2024 · ai assisted · 6/10 importance

• CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions

  Genome-wide prioritization of genetic variants across molecular functions is needed to aid assessment of clinical findings and genetic studies.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer

  Reconstructing high-fidelity MRI images from highly under-sampled k-space data is a challenging ill-posed inverse problem.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI

  Clinical knee MRI has long acquisition times, and it was unclear whether deep learning reconstruction could work reliably in everyday practice.

  Medicine & Health · 2023 · partially solved · 6/10 importance

• A Deep Learning-Based Framework for Retinal Disease Classification

  Automatic detection and classification of retinal disease states from OCT images was needed to assist diagnosis.

  Medicine & Health · 2023 · solved · 6/10 importance

• Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis

  Identifying eligible patients for cancer clinical trials is a bottleneck; AI tools were assessed for their predictive accuracy in trial enrollment.

  Medicine & Health · 2023 · partially solved · 6/10 importance

• Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI

  Automatically detecting Alzheimer's dementia from brain MRI scans to support early diagnosis.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

  Rapid, label-free identification of pathogenic bacteria and their antibiotic resistance at single-cell resolution, avoiding slow culture and susceptibility assays.

  Medicine & Health · 2023 · partially solved · 6/10 importance

• Deep structured learning for variant prioritization in Mendelian diseases

  Identifying the disease-causing genetic variant among many candidates in Mendelian diseases is slow and difficult.

  Medicine & Health · 2023 · solved · 6/10 importance

• Deep learning integrates histopathology and proteogenomics at a pan-cancer level

  Predicting clinical outcomes and molecular features (e.g., TP53 status) from routine H&E pathology slides alone, and explaining which histology features are predictive, was difficult.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Image based deep learning in 12-lead ECG diagnosis

  In many clinical settings, especially remote and regional ones, ECGs exist only as printed paper or digital images, while most machine learning diagnostic tools require raw signal data.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Learning to predict RNA sequence expressions from whole slide images with applications for search and classification

  Molecular tests like bulk RNA-seq are expensive, time-consuming and require additional tissue, limiting their use alongside pathology slide images.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Deep Transfer Learning for Chronic Obstructive Pulmonary Disease Detection Utilizing Electrocardiogram Signals

  Diagnosing COPD normally requires spirometer tests and a lengthy hospital workup, so the disease often goes undetected until its final stage.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images

  Evidence on whether deep learning can predict cardiovascular disease risk from retinal images was scattered and needed systematic synthesis.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Deep-learning based breast cancer detection for cross-staining histopathology images

  Color variation between H&E and fluorescent staining makes cross-analysis of breast tissue images difficult for computer-assisted AI algorithms.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke

  Predicting the 3-month functional outcome of acute ischemic stroke patients is needed to establish suitable treatment plans, but is clinically difficult.

  Medicine & Health · 2023 · ai assisted · 6/10 importance

• Faecal microbiome-based machine learning for multi-class disease diagnosis

  Shared microbial signatures across diseases make accurate single-disease diagnosis from faecal microbiome data difficult, and non-invasive diagnostic approaches are needed.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

  Most genetic variants linked to eye diseases are noncoding, making their function and target genes hard to interpret.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Swarm learning for decentralized artificial intelligence in cancer histopathology

  Training robust AI on medical images needs large multicentric datasets that face practical, ethical and legal obstacles to sharing.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification

  Skin lesions look highly similar in early stages of skin cancer, causing delayed diagnosis.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study

  Early prediction of in-hospital mortality in critically ill sepsis patients is difficult for clinicians.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Multimodal deep learning applied to classify healthy and disease states of human microbiome

  Classifying healthy versus disease states from human microbiome metagenomic data, where existing tools lacked accuracy needed for diagnostics.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• Fast and scalable search of whole-slide images via self-supervised deep learning

  Searching large repositories of gigapixel whole-slide images for morphologically similar slides was too slow because retrieval speed scaled with repository size.

  Medicine & Health · 2022 · solved · 6/10 importance

• A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer

  It was unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer, a common and deadly disease.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

  Deep learning is being widely applied to ECG data for clinical diagnosis, but the state of the field needed systematic assessment.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor

  Retrieving hit/lead compounds with novel scaffolds for a given biological target in early drug development is hard, and generative models' capacity to produce wet-lab-validated, target-specific molecules was largely unverified.

  Medicine & Health · 2022 · solved · 6/10 importance

• A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies

  Gleason grading of prostate core needle biopsies is subjective and depends on pathologist expertise, and deep learning systems degrade on slides from other labs.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

  Manual cervical cancer screening of liquid-based cytology slides is limited by human resources, motivating automated diagnosis of digitised whole slide images.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• Assessment of deep learning assistance for the pathological diagnosis of gastric cancer

  Improving pathologists' accuracy in diagnosing gastric cancer from whole-slide images.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Multimodal survival prediction in advanced pancreatic cancer using machine learning

  Existing risk scores were insufficient to assess individual survival risk in patients with advanced pancreatic ductal adenocarcinoma.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

  Early diagnosis and survival prediction of bladder cancer from whole-slide digitised histological images, to reduce mortality.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• A novel approach for skin lesion symmetry classification with a deep learning model

  Early skin cancer diagnosis requires assessing lesion symmetry in dermoscopic images, which is difficult to do consistently and automatically.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Prediction and risk assessment of sepsis-associated encephalopathy in ICU based on interpretable machine learning

  Sepsis-associated encephalopathy is a dangerous complication of sepsis in ICU patients with high mortality, and clinicians lacked a tool to predict which patients would develop it.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Deep learning for survival analysis in breast cancer with whole slide image data

  Predicting breast cancer survival from whole slide images is difficult due to data size, complexity, and limited training data.

  Medicine & Health · 2022 · partially solved · 6/10 importance

• A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning

  Manual microscopic diagnosis of prostate adenocarcinoma across large numbers of needle biopsy specimens is time-consuming and limited by human resources, so rapid, accurate screening techniques are needed.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Automatic Detection of Left Ventricular Dilatation and Hypertrophy from Electrocardiograms Using Deep Learning

  Detecting left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) from 12-lead ECGs to improve heart failure screening.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video

  At the onset of major vascular injury in minimally invasive surgery, surgeons cannot predict blood loss or whether hemorrhage can be controlled.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

  Predicting final infarcted brain tissue in acute ischemic stroke from admission CT or MRI, which is needed to guide treatment decisions.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• A Focus on the Role of DSC-PWI Dynamic Radiomics Features in Diagnosis and Outcome Prediction of Ischemic Stroke

  Accurately detecting ischemic stroke, assessing neurological impairment, and predicting patient outcome from brain perfusion imaging.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

  Arbovirus outbreaks are hard to anticipate, and public health decision-makers need forecasting models of disease and vector dynamics.

  Medicine & Health · 2022 · ai assisted · 6/10 importance

• A comparison of machine learning algorithms for diabetes prediction

  Diabetes has no permanent cure, so early detection from patient data is needed.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning

  Pathologists visually inspect whole-slide lymph node images to count metastatic lymph nodes, yet patients at the same N stage still have dramatically different outcomes.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Clinical use of machine learning‐based pathomics signature for diagnosis and survival prediction of bladder cancer

  Diagnosing bladder cancer accurately from tissue slides, where visual inspection by pathologists is insufficient.

  Medicine & Health · 2021 · partially solved · 6/10 importance

• Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram

  Automated ECG diagnosis must detect multiple co-occurring heart abnormalities in one signal, and deep learning's black-box nature blocks high-risk clinical use.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome

  QTc measurement has low positive predictive value for identifying drugs at high risk of torsade de pointes and diagnosing congenital long QT syndrome.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Automated Explainable Multidimensional Deep Learning Platform of Retinal Images for Retinopathy of Prematurity Screening

  ROP diagnosis relies on indirect ophthalmoscopy by experienced ophthalmologists, which may limit early detection and timely treatment.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Machine learning-based prediction of survival prognosis in cervical cancer

  Clinical features alone are insufficient to forecast cervical cancer prognosis, so better predictive models are needed to improve patient management.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• A Deep Learning–Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome

  Brugada syndrome is a rare inherited arrhythmia and a major cause of sudden cardiac death in young people, requiring early identification of its type 1 ECG pattern so patients can receive life-saving therapy.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review

  Detecting structural cardiac pathologies (LV systolic dysfunction, hypertrophy, ischaemia) from resting electrocardiograms.

  Medicine & Health · 2021 · partially solved · 6/10 importance

• Detecting cardiomyopathies in pregnancy and the postpartum period with an electrocardiogram-based deep learning model

  Cardiomyopathy is among the most common acquired cardiovascular diseases during pregnancy and the postpartum period, and needs better detection tools.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry

  Manual collection of clinical variables for machine learning prediction of major adverse cardiac events from SPECT myocardial perfusion imaging is time-consuming and error-prone.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Uncertainty estimation for deep learning-based automated analysis of 12-lead electrocardiograms

  Deep neural networks for automated ECG interpretation show encouraging results, but whether such results can be trusted is paramount for clinical implementation.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• 3Cnet: pathogenicity prediction of human variants using multitask learning with evolutionary constraints

  Interpreting human genetic variants for disease diagnosis is slow, costly, and existing tools suffer from overfitting due to limited clinical data.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

  Detecting and classifying breast cancer metastasis in histopathology whole slide images is difficult and needs computer-aided second opinions.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Automated machine learning for endemic active tuberculosis prediction from multiplex serological data

  Active tuberculosis serological diagnosis requires interpreting complex multi-antibody data across variable immune responses, and building suitable algorithms manually is slow.

  Medicine & Health · 2021 · ai assisted · 6/10 importance

• Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest X-ray

  Automatically detecting bacterial and viral pneumonia from digital chest X-ray images for early diagnosis.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• A unified connectomic target for deep brain stimulation in obsessive-compulsive disorder

  Surgeons use multiple competing deep brain stimulation targets for obsessive-compulsive disorder, and it is unclear which location produces clinical improvement.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• A deep learning model to predict RNA-Seq expression of tumours from whole slide images

  Predicting tumour RNA-Seq expression and molecular features from histology whole-slide images without expert annotation or sequencing.

  Medicine & Health · 2020 · partially solved · 6/10 importance

• Early detection of type 2 diabetes mellitus using machine learning-based prediction models

  Detecting undiagnosed type 2 diabetes early, when existing screening tests rely on simplified regression-based scoring formulas.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Deep learning and the electrocardiogram: review of the current state-of-the-art

  Traditional ECG analysis was limited for detecting arrhythmia, ischemia, and cardiomyopathy at scale in big healthcare datasets.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures

  Detecting and classifying dangerous melanoma skin lesions from dermoscopic images is difficult and error-prone.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features

  Alzheimer's disease and vascular dementia share symptoms and MRI signs, making clinical differential diagnosis difficult and sometimes controversial.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

  MRI acquisition is slow, so images must be reconstructed from undersampled k-space data to accelerate scanning.

  Medicine & Health · 2020 · partially solved · 6/10 importance

• Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning

  Histopathological diagnosis of lymphomas, especially follicular lymphoma, is challenging, requires expertise or centralized review, and depends on tissue-section technical quality.

  Medicine & Health · 2020 · partially solved · 6/10 importance

• Rhapsody: predicting the pathogenicity of human missense variants

  Predicting whether human missense variants are pathogenic remains difficult for clinical molecular diagnostics.

  Medicine & Health · 2020 · partially solved · 6/10 importance

• Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

  Matching lung cancer patients to eligible clinical trials requires evaluating thousands of patient attributes against complex trial eligibility criteria, which is slow to do manually.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

  During the acute COVID-19 outbreak, clinicians needed to determine disease severity in patients using chest X-rays and to test whether deep learning could support this.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

  Existing machine learning tools imprecisely discriminate benign from pathogenic rare variants in inherited cardiac conditions.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Deep Learning–Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation

  Detecting early renal function impairment non-invasively, using retinal fundus images instead of blood tests.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery

  Designing small molecules with desired properties relies on human intuition, slowing drug discovery design cycles.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• IGRNet: A Deep Learning Model for Non-Invasive, Real-Time Diagnosis of Prediabetes through Electrocardiograms

  Prediabetes has mild, easily overlooked symptoms and can progress to diabetes without early intervention, so non-invasive early detection is needed.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Machine Learning Explainability in Breast Cancer Survival

  Black-box ML models that predict breast cancer survival are hard for clinicians to trust, hindering clinical adoption.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans

  Visual detection of early ischemic changes in posterior circulation stroke on non-contrast CT is unreliable due to reader variability, artifacts, and low sensitivity.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images

  Detecting breast cancer early from histopathology images and reducing the diagnostic burden on doctors.

  Medicine & Health · 2020 · ai assisted · 6/10 importance

• Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis

  Whole-slide lung cancer image classification is blocked by scarce pixel-level annotations, heterogeneous tumor patterns, and gigapixel image sizes.

  Medicine & Health · 2019 · ai assisted · 6/10 importance

• Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

  Hyperkalemia is common, potentially fatal, and often asymptomatic in chronic kidney disease patients, while guideline-directed blood potassium monitoring is underused.

  Medicine & Health · 2019 · ai assisted · 6/10 importance

• RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge

  There was no standardized way to judge how well AI methods detect retinal fluid in OCT scans.

  Medicine & Health · 2019 · partially solved · 6/10 importance

• Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM)

  Improving early diagnosis of melanoma by automatically segmenting skin lesions and classifying them as melanoma or benign on dermoscopy images.

  Medicine & Health · 2019 · ai assisted · 6/10 importance

• Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

  Diagnosing diabetic retinopathy requires labor-intensive manual analysis of retinal blood vessels by ophthalmologists.

  Medicine & Health · 2019 · ai assisted · 6/10 importance

• Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods

  Conventional statistical methods poorly predict survival outcomes in epithelial ovarian cancer patients.

  Medicine & Health · 2019 · partially solved · 6/10 importance

• Application of Machine Learning Models for Survival Prognosis in Breast Cancer Studies

  Predicting breast cancer patient survival time from clinical data to support therapy and management decisions.

  Medicine & Health · 2019 · ai assisted · 6/10 importance

• Deep learning for undersampled MRI reconstruction

  Standard MRI is slow because it requires time-consuming full k-space data acquisition.

  Medicine & Health · 2018 · solved · 6/10 importance

• RADnet: Radiologist level accuracy using deep learning for hemorrhage detection in CT scans

  Automatically detecting brain hemorrhage in CT scans to support radiologist diagnosis.

  Medicine & Health · 2018 · partially solved · 6/10 importance

• Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

  Detecting tuberculosis from chest X-ray images via computer-aided diagnosis, particularly when the available dataset is small and class-imbalanced.

  Medicine & Health · 2018 · ai assisted · 6/10 importance

• Data augmentation importance for classification of skin lesions via deep learning

  Detecting malignant skin lesions (melanoma) from images is hard when training data is insufficient; the study asked whether data augmentation improves deep-learning classifiers.

  Medicine & Health · 2018 · ai assisted · 6/10 importance

• Lung Cancer Survival Prediction via Machine Learning Regression, Classification, and Statistical Techniques

  Predicting survival time in months for lung cancer patients is inaccurate for longer survival horizons, since prior models performed well only for survival under 6 months.

  Medicine & Health · 2018 · ai assisted · 6/10 importance

• Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning

  Identifying patients with suspected infection early at emergency department triage is difficult, and the study tested whether free-text data adds predictive value beyond vitals and demographics.

  Medicine & Health · 2017 · ai assisted · 6/10 importance

• Developing a dengue forecast model using machine learning: A case study in China

  Accurate and timely forecasts of dengue incidence in China were lacking despite expanded affected areas and increased incidence.

  Medicine & Health · 2017 · ai assisted · 6/10 importance

• A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer

  Detecting breast cancer automatically from histopathology images.

  Medicine & Health · 2017 · ai assisted · 6/10 importance

• Multivariate Analysis and Machine Learning in Cerebral Palsy Research

  Early diagnosis of cerebral palsy in high-risk infants, plus identification of risk factors, movement impairment, and surgical outcome prediction.

  Medicine & Health · 2017 · ai assisted · 6/10 importance

• Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

  Automated detection of nuclei in high-resolution breast cancer histopathology images, needed for computer-assisted grading such as the Nottingham Histologic Score.

  Medicine & Health · 2015 · ai assisted · 6/10 importance

• Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry

  Predicting cancer patient survival accurately using routinely collected digital records, as an enhancement to conventional prognostic methods.

  Medicine & Health · 2014 · ai assisted · 6/10 importance

• The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus

  Whether reduced susceptibility to biocides co-selects for antibiotic resistance in natural clinical isolates of Staphylococcus aureus was unclear.

  Medicine & Health · 2013 · ai assisted · 6/10 importance

• LLM-assisted systematic review of large language models in clinical medicine

  The rapid flood of clinical LLM studies (roughly 3.2 papers per day) makes manual curation and review of the evidence base infeasible.

  Medicine & Health · 2026 · ai assisted · 5/10 importance

• The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review

  Researchers lacked an overview of how large language models are actually being used to automate and assist scientific literature reviews.

  Medicine & Health · 2025 · ai assisted · 5/10 importance

• Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

  Public benchmarks suffer data contamination and LLM-as-judge self-preference, leaving no reliable way to evaluate and compare clinical large language models.

  Medicine & Health · 2025 · partially solved · 5/10 importance

• Evaluating clinical AI summaries with large language models as judges

  Human review, the gold standard for checking AI-generated EHR summaries, is costly and slow, making reliable evaluation of clinical summaries difficult.

  Medicine & Health · 2025 · solved · 5/10 importance

• Large Language Model Assistant for Emergency Department Discharge Documentation

  Emergency department discharge documentation is time-consuming and often incomplete, burdening physicians and risking patient safety.

  Medicine & Health · 2025 · ai assisted · 5/10 importance

• Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review

  Radiologists face heavy MRI workloads with long scanning and reading times; the review asks whether AI can improve productivity.

  Medicine & Health · 2025 · ai assisted · 5/10 importance

• A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images

  Limited access to dermatological expertise creates a need for automated skin lesion segmentation and classification from dermoscopic images.

  Medicine & Health · 2025 · ai assisted · 5/10 importance

• Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

  Designing peptide inhibitors that disrupt specific protein-protein interactions, here β-catenin with TCF/LEF transcription factors, for therapeutic discovery.

  Medicine & Health · 2024 · partially solved · 5/10 importance

• Optimization of hepatological clinical guidelines interpretation by large language models: a retrieval augmented generation-based framework

  Clinical decision support systems need accurate interpretation of medical guidelines for chronic Hepatitis C virus management.

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

  Characterising the gut microbiome for precision medicine requires extracting insight from complex multi-omics datasets (metagenomics, metatranscriptomics, glycomics, metabolomics).

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Large language models for structured reporting in radiology: past, present, and future

  Structured reporting in radiology would standardize and improve report quality, but widespread adoption has remained limited.

  Medicine & Health · 2024 · partially solved · 5/10 importance

• Distilling large language models for matching patients to clinical trials

  Matching patients to clinical trials requires analyzing eligibility against complex inclusion and exclusion criteria, which is slow and hard to scale.

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms

  Predicting health insurance claims in the USA to improve claim processing and reduce insurance costs.

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos

  Deep-learning-powered surgical video analysis for cataract surgery was held back by a lack of large-scale annotated datasets for workflow analysis and detecting post-operative irregularities.

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Application of machine learning in breast cancer survival prediction using a multimethod approach

  Predicting 5-year survival of women with breast cancer is needed to set appropriate care plans in Iran.

  Medicine & Health · 2024 · ai assisted · 5/10 importance

• Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark

  Surgical workflow and skill analysis algorithms trained on single-center data lacked multicenter validation of generalizability.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review

  Predicting cardiovascular disease risk in patients with type 2 diabetes using machine learning-based prediction models.

  Medicine & Health · 2023 · partially solved · 5/10 importance

• A Survey of Automatic Speech Recognition for Dysarthric Speech

  People with dysarthric speech (discontinuous, uncontrolled volume, slow or explosive pronunciation) are poorly served by standard automatic speech recognition.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating Large-Scale AI and Large Language Models for Comparative Analysis with ChatGPT

  Chest X-ray interpretation by AI needed validation; the study compared an assistive CXR reading model against ChatGPT for diagnostic accuracy and clinical utility.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

  Interpreting the pathogenicity of missense variants in mitochondrial DNA is difficult due to highly variable clinical manifestations.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

  Need for a comprehensive overview of AI applications in knee osteoarthritis diagnosis, prognosis and imaging.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review

  Assessing whether machine learning can reliably predict which stroke patients will benefit from rehabilitation treatment.

  Medicine & Health · 2023 · ai assisted · 5/10 importance

• Medical image segmentation using deep learning: A survey

  Accurate segmentation of medical images is needed for clinical analysis, and the deep learning literature on it is large and disorganized.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Deep learning in macroscopic diffuse optical imaging

  Analyzing macroscopic diffuse optical images (optical property retrieval, fluorescence lifetime imaging, tomography) with conventional inverse solvers is slow and limited in reconstruction quality.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Human disease prediction from microbiome data by multiple feature fusion and deep learning

  Predicting human diseases from microbiome (metagenomic) data suffers information loss and severe overfitting because datasets are high-dimensional with few samples.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Deep learning model-assisted detection of kidney stones on computed tomography

  Detecting kidney stones of different sizes on unenhanced CT images across axial, coronal and other planes.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine Learning Approaches With Fingerprint Amalgamation and Data Balancing

  Predicting whether existing drugs can cross the blood-brain barrier is difficult, hindering repurposing of drugs for neurological diseases.

  Medicine & Health · 2022 · partially solved · 5/10 importance

• Artificial Intelligence–Driven Ultra-Fast Superresolution MRI

  Long MRI pulse sequence acquisition times restrict patient tolerability and access in musculoskeletal imaging.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification

  Skin lesion classification needs high-performance, low-cost data augmentation so intelligent skin cancer screening devices can work in rural or underdeveloped communities.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Machine learning models for prediction of adverse events after percutaneous coronary intervention

  Clinicians need accurate prediction of major adverse events (acute kidney injury, bleeding, in-hospital mortality) after percutaneous coronary intervention to improve decisions.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• The impact of chemotherapy and survival prediction by machine learning in early Elderly Triple Negative Breast Cancer (eTNBC): a population based study from the SEER database

  Determining whether elderly early triple-negative breast cancer patients benefit from chemotherapy and predicting their survival.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Surgical instrument detection and tracking technologies: Automating dataset labeling for surgical skill assessment

  Assessing surgeons' technical skill from video is time-consuming and subjective, and labeling raw surgical video datasets for skill analysis is hard.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction

  GAN-based MRI reconstruction from under-sampled data normally requires large training sets of raw k-space patient data, which is difficult to gather in clinical practice.

  Medicine & Health · 2022 · partially solved · 5/10 importance

• A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram

  Acute pericarditis is easily confused with STEMI in emergency-department chest-pain patients, risking misdiagnosis from 12-lead ECGs.

  Medicine & Health · 2022 · ai assisted · 5/10 importance

• Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

  ML survival models are often disregarded in oncology due to lack of transparency, leaving Cox regression as the standard.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• Deep neural network correlation learning mechanism for CT brain tumor detection

  Detecting brain tumors in CT scans accurately and efficiently to support clinical diagnosis.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

  Skin lesion diagnosis is a growing research area, and the paper reviews the diagnostic accuracy of computer-aided ML/DL systems developed over the last five years.

  Medicine & Health · 2021 · partially solved · 5/10 importance

• It takes guts to learn: machine learning techniques for disease detection from the gut microbiome

  Predicting human diseases from gut microbiome data is difficult, with ML showing promise for some conditions but lacking efficacy for others.

  Medicine & Health · 2021 · partially solved · 5/10 importance

• Benchmarking deep learning splice prediction tools using functional splice assays

  Predicting whether variants in noncanonical splice sites and deep intronic regions disrupt splicing is difficult, complicating diagnosis of hereditary disorders.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images

  A subset of small transbronchial lung biopsy cases cannot be confidently classified by pathologists from H&E slides alone, requiring extra immunohistochemistry or surgery.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• Effectiveness of Transfer Learning for Deep Learning-Based Electrocardiogram Analysis

  Individual researchers cannot collect the large labeled biosignal datasets needed to train deep learning models for electrocardiogram rhythm analysis.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification

  ECG diagnosis algorithms need large labeled datasets, but privacy concerns and annotation costs cause a shortage of labeled ECG data.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• Detection of sarcopenic obesity and prediction of long‐term survival in patients with gastric cancer using preoperative computed tomography and machine learning

  Manually measuring body composition from CT scans to identify sarcopenic obesity in gastric cancer patients is slow and has only been studied in small populations; the study assessed its prevalence and prognostic value at scale.

  Medicine & Health · 2021 · ai assisted · 5/10 importance

• DeepMicro: deep representation learning for disease prediction based on microbiome data

  Predicting human diseases from microbiome data is difficult because the data is extremely high-dimensional (hundreds of thousands of features) while sample sizes are small, making learned models sparse and weak.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks

  Multi-contrast MRI scans take excessive time because each added contrast lengthens acquisition, limiting clinical use.

  Medicine & Health · 2020 · partially solved · 5/10 importance

• Machine learning applied to retinal image processing for glaucoma detection: review and perspective

  Detecting and diagnosing glaucoma from retinal images using machine learning classification algorithms.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• A deep learning, image based approach for automated diagnosis for inflammatory skin diseases

  Inflammatory skin diseases such as psoriasis, eczema and atopic dermatitis are easily misdiagnosed in clinical practice.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based Multiple Surgical Instrument Tracking in Training and Actual Operations

  Surgeons' skills in robotic surgery are hard to evaluate automatically and quantitatively because they depend on instrument movement.

  Medicine & Health · 2020 · partially solved · 5/10 importance

• Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support

  Classifying dermoscopy images into seven lesion classes is difficult, and multi-class classification accuracy needed improvement.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases

  Diagnosing multiple-lesion skin diseases such as acne, rosacea, psoriasis, eczema and cutaneous T-cell lymphoma is challenging and needs computer-aided support.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning

  Diagnosing heart failure from chest X-ray images is difficult and rarely automated. The study tested whether deep learning could diagnose heart failure from chest X-rays.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Skin Lesion Classification using Deep Learning Architectures

  Classifying skin lesions from images to enable early detection of lesion types that can become malignant.

  Medicine & Health · 2020 · partially solved · 5/10 importance

• Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

  The qSOFA score has limited accuracy for predicting mortality in emergency-department patients with suspected infection.

  Medicine & Health · 2020 · ai assisted · 5/10 importance

• Faculty Opinions recommendation of Deep learning enables rapid identification of potent DDR1 kinase inhibitors.

  Discovering potent inhibitors of DDR1 kinase, a target implicated in fibrosis and other diseases, is slow using traditional methods.

  Medicine & Health · 2019 · partially solved · 5/10 importance

• Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers

  Brain MR images acquired with fewer acquisitions suffer from noise, degrading diagnostic image quality.

  Medicine & Health · 2019 · solved · 5/10 importance

• Deep learning-based automated detection of retinal diseases using optical coherence tomography images

  Automated detection of four retinal conditions (CNV, DME, DRUSEN, NORMAL) in OCT images for computer-aided diagnosis.

  Medicine & Health · 2019 · solved · 5/10 importance

• An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

  Group-level glioblastoma survival statistics are well defined, but predicting individual patient survival is difficult due to variation within strata.

  Medicine & Health · 2019 · ai assisted · 5/10 importance

• Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

  Predicting cancer recurrence and survival one, three, and five years after radical cystectomy in bladder cancer patients.

  Medicine & Health · 2019 · ai assisted · 5/10 importance

• Multimodal skin lesion classification using deep learning

  Prior automated skin lesion classifiers used only a single macroscopic image, limiting diagnostic performance in real-world clinical scenarios.

  Medicine & Health · 2018 · ai assisted · 5/10 importance

• Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery

  Comparing generative models that design new drug molecules was unreliable because existing evaluation metrics were flawed.

  Medicine & Health · 2018 · partially solved · 5/10 importance

• CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning

  Automatically detecting anomalies in chest X-ray images via deep learning suffered from over-fitting and low transfer efficiency when fine-tuning existing networks.

  Medicine & Health · 2018 · ai assisted · 5/10 importance

• Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

  Detecting and classifying cell nuclei in routine colon cancer histology images, which is difficult due to cellular heterogeneity.

  Medicine & Health · 2016 · ai assisted · 5/10 importance

• Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

  Predicting pharmacological properties and therapeutic uses of drugs, and repurposing existing drugs, is slow and costly.

  Medicine & Health · 2016 · ai assisted · 5/10 importance

• Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

  Evaluating surgeon skill in robotic-assisted minimally invasive surgery has relied on subjective human judgment rather than objective measures.

  Medicine & Health · 2016 · ai assisted · 5/10 importance

• Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network

  Predicting the site of epoxidation on drug-like molecules, since epoxide reactive metabolites cause drug toxicity.

  Medicine & Health · 2015 · partially solved · 5/10 importance

• Automated generation of discharge summaries: leveraging large language models with clinical data

  Writing discharge summaries is time-consuming for physicians; this study tested automating generation of German discharge summaries from structured clinical data.

  Medicine & Health · 2025 · ai assisted · 4/10 importance

• Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review

  Surgical training assessment and evaluation rely on traditional methods that may be improved by data-driven AI techniques.

  Medicine & Health · 2025 · ai assisted · 4/10 importance

• Drug target prediction through deep learning functional representation of gene signatures

  Machine learning analyses of gene signatures rely on matching gene identities and ignore preexisting knowledge of gene functions, limiting compound-target prediction and mechanism-of-action studies.

  Medicine & Health · 2024 · partially solved · 4/10 importance

• Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence

  Clinical research is hindered by the high cost of collecting large patient datasets and privacy/regulatory barriers to accessing existing data.

  Medicine & Health · 2024 · partially solved · 4/10 importance

• Evaluation Framework of Large Language Models in Medical Documentation: Development and Usability Study

  There was no established framework or quality standards for assessing accuracy and clinical applicability of LLM-generated emergency department records.

  Medicine & Health · 2024 · ai assisted · 4/10 importance

• Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers

  Key genes driving diabetic foot ulcer pathogenesis and treatment response were not well identified.

  Medicine & Health · 2024 · ai assisted · 4/10 importance

• Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support

  Understanding why clinicians override CDS alerts required summarizing large volumes of user alert comments.

  Medicine & Health · 2024 · ai assisted · 4/10 importance

• Integration of Localized, Contextual, and Hierarchical Features in Deep Learning for Improved Skin Lesion Classification

  Classifying skin lesions from dermoscopic images is difficult because existing methods struggle with complex information and long-range dependencies.

  Medicine & Health · 2024 · ai assisted · 4/10 importance

• Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP

  Nasopharyngeal cancer patients attain different outcomes and need prognostic stratification into low and high survival groups.

  Medicine & Health · 2023 · ai assisted · 4/10 importance

• Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning

  Identifying and subtyping amyloidosis (e.g., immunoglobulin light chain vs amyloid A) in kidney tissue normally requires special stains, immunolabeling, electron microscopy, or mass spectrometry.

  Medicine & Health · 2023 · ai assisted · 4/10 importance

• Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning

  Measuring three-directional ground reaction forces during walking normally requires lab force plates; a wearable shoe sole sensor system was developed to predict them.

  Medicine & Health · 2023 · partially solved · 4/10 importance

• Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms

  Postoperative delirium after cardiovascular surgery is common and linked to adverse events; high-risk patients need preoperative identification.

  Medicine & Health · 2023 · ai assisted · 4/10 importance

• Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction

  Thin-slice prostate MRI could improve cancer diagnostics but unacceptably prolongs acquisition time.

  Medicine & Health · 2023 · partially solved · 4/10 importance

• Preserving privacy in surgical video analysis using a deep learning classifier to identify out-of-body scenes in endoscopic videos

  Endoscopic surgical videos can record privacy-sensitive out-of-body scenes, exposing patients and operating room staff when videos are used for education and research.

  Medicine & Health · 2023 · ai assisted · 4/10 importance

• Deep learning acceleration of multiscale superresolution localization photoacoustic imaging

  Superresolution photoacoustic imaging achieves very high spatial resolution but requires superimposing many raw image frames, sacrificing temporal resolution and making imaging slow.

  Medicine & Health · 2022 · solved · 4/10 importance

• Image quality assessment for machine learning tasks using meta-reinforcement learning

  Medical images vary in usefulness for downstream ML tasks; a method was needed to assess task amenability and adapt quickly to new datasets.

  Medicine & Health · 2022 · partially solved · 4/10 importance

• Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review

  Histological interpretation of melanocytic tumors is challenging and subject to interobserver variability between dermatopathologists.

  Medicine & Health · 2022 · ai assisted · 4/10 importance

• Multimodal deep learning for biomedical data fusion: a review

  Biomedical data are increasingly multimodal, and researchers lack organized guidance on which deep learning fusion strategies to use.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• Automation of surgical skill assessment using a three-stage machine learning algorithm

  Manual assessment of surgeons' skills is time-consuming, subjective and prone to inconsistent interpretation.

  Medicine & Health · 2021 · partially solved · 4/10 importance

• Automated claustrum segmentation in human brain MRI using deep learning

  The claustrum's delicate, sheet-like structure made it infeasible to segment with conventional methods in human brain MRI, limiting in vivo research.

  Medicine & Health · 2021 · partially solved · 4/10 importance

• Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

  Deep learning ECG predictions lack human-understandable explanations, limiting clinical trust and knowledge discovery.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards

  How problem framing choices (prediction windows, observation windows, event triggers) affect the performance and clinical usability of machine learning risk prediction models, illustrated with sepsis in general wards.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• A Speech Command Control-Based Recognition System for Dysarthric Patients Based on Deep Learning Technology

  Dysarthric patients struggle to use voice control on mobile devices, and existing ASR systems require large computation, raising implementation costs.

  Medicine & Health · 2021 · partially solved · 4/10 importance

• Pill Detection Model for Medicine Inspection Based on Deep Learning

  Detecting individual pills in images containing multiple pills is difficult when training data is limited and pill combinations grow exponentially with pill types.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• MR Images, Brain Lesions, and Deep Learning

  White matter hyperintensities in brain MR images need to be classified and detected to support computer-aided diagnosis of ischemic stroke and demyelinating diseases.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning

  Identifying poorly differentiated colorectal adenocarcinoma in endoscopic biopsy whole slide images is difficult and could benefit from AI tools for pathologists.

  Medicine & Health · 2021 · ai assisted · 4/10 importance

• The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning

  Predicting 5-year survival among lung cancer survivors, and whether health-related quality of life (HRQOL) data improves prediction beyond clinical and sociodemographic variables.

  Medicine & Health · 2020 · ai assisted · 4/10 importance

• Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network

  Automatic speech recognizers perform poorly on dysarthric speech, making voice technology unreliable for affected speakers.

  Medicine & Health · 2020 · partially solved · 4/10 importance

• The Auto-eFACE: Machine Learning–Enhanced Program Yields Automated Facial Palsy Assessment Tool

  Facial palsy assessment relied on subjective, nonstandardized clinician-graded scales prone to observer bias, making treatment comparison unreliable.

  Medicine & Health · 2020 · partially solved · 4/10 importance

• Dysarthric Speech Recognition using Convolutional Recurrent Neural Networks

  Automatic speech recognition performs poorly on dysarthric (impaired) speech, which is unintelligible, irregular, and data-scarce.

  Medicine & Health · 2020 · partially solved · 4/10 importance

• Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning

  Doctors lacked a tool to predict overall survival for gallbladder cancer patients from pre-treatment CT scans. The study built a machine learning model combining CT features with clinical information to predict survival.

  Medicine & Health · 2020 · ai assisted · 4/10 importance

• Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning

  Pneumonia diagnosis from chest X-rays is subjective and can be confused with other diseases.

  Medicine & Health · 2019 · ai assisted · 4/10 importance

• Deep learning-based survival prediction of oral cancer patients

  Traditional Cox proportional hazards models may be too simplistic to accurately predict survival outcomes for oral squamous cell carcinoma patients.

  Medicine & Health · 2019 · ai assisted · 4/10 importance

• Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models

  Pathological estimation of viable versus necrotic tumor after chemotherapy in osteosarcoma requires laborious analysis of whole slide images.

  Medicine & Health · 2019 · partially solved · 4/10 importance

• Classification of Paediatric Inflammatory Bowel Disease using Machine Learning

  Accurate diagnosis of paediatric inflammatory bowel disease subtypes (Crohn's, ulcerative colitis, IBDU) is needed for prompt, effective treatment.

  Medicine & Health · 2017 · partially solved · 4/10 importance

• Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

  Automated detection of multiple retinal diseases from fundus photographs using computer-aided diagnosis.

  Medicine & Health · 2017 · partially solved · 4/10 importance

• Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

  Surgical expertise in robot-assisted surgery is difficult to assess objectively; the study aimed to automatically classify surgeons' skill levels.

  Medicine & Health · 2023 · ai assisted · 3/10 importance

• Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms

  Poststroke depression is a major psychiatric complication after stroke, and predicting which patients will develop it is difficult.

  Medicine & Health · 2022 · ai assisted · 3/10 importance

• Electrocardiogram Quality Assessment Using Unsupervised Deep Learning

  Noise and disturbances hinder effective interpretation of recorded ECG signals, and existing quality indicators rely on human-defined properties.

  Medicine & Health · 2021 · ai assisted · 3/10 importance

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