SolvedByAI

Queue

628 cases were harvested and are backed by a real paper, but they fail the test the index publishes on. Each one is listed here with the reason, so the exclusion can be checked rather than trusted.

All 628 assisted 358 not a result 183 unsupported 87
Paper Year Cited Why it is held
A comparative analysis of machine learning classifiers for stroke prediction... Healthcare Analytics 2022 116 unsupported no abstract available for this DOI
Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Marker... Frontiers in Physiology 2021 76 assisted The ML (SVM classifier) was one component in a human-run proteomics workflow, humans generated the mass spectrometry data, selected/validated the biomarkers via MRM assays, and performed the drug screening, so the AI only informed the human-driven discovery rather than producing the result itself. “we employed supervised machine learning (ML) approaches using a linear support vector machine model to identify the classifiers of patients with non-s...”
Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Bra... Applied Computing and Informatics 2020 57 assisted The authors ran a human-driven forecasting study and merely employed an off-the-shelf tool (Fb-Prophet) as one component, reporting its projection accuracy on an existing epidemic-forecasting task rather than the AI itself contributing a new end-to-end capability. “the authors adopted a machine learning (ML) model called Fb-Prophet”
A Systematic Review of Machine Learning and IoT Applied to the Prediction an... Healthcare 2023 55 not a result This is a systematic review summarizing 164 existing papers on ML/IoT for cardiovascular disease, with no primary AI-produced result of its own. “This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease.”
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus... IEEE Access 2021 292 not a result This is a survey/review paper that overviews and categorizes existing deep learning systems for COVID-19 diagnosis rather than presenting a new AI-produced result. “This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer...”
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Che... Frontiers in Medicine 2020 287 assisted The deep learning classifier reports accuracy on an existing image-classification task and is explicitly positioned as a pre-screening/triage aid within a human clinical workflow, with RT-PCR remaining the definitive diagnostic test. “The proposed deep learning-based decision-tree classifier may be used in pre-screening patients to conduct triage and fast-track decision making befor...”
Detection and analysis of COVID-19 in medical images using deep learning tec... Scientific Reports 2021 158 assisted The paper reports classification accuracies of pre-trained CNN models on existing public datasets and explicitly frames the AI output as an aid to human clinicians rather than an end-to-end solved diagnostic capability. “our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19”
An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Usi... Cognitive Computation 2021 114 assisted The machine learning (XGBoost) served as a biomarker-identification and risk-scoring component inside a human-run retrospective clinical study, with the resulting nomogram explicitly framed as a tool to inform doctors' patient-stratification decisions rather than the AI itself performing the clinical task end to end. “The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to im...”
Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Ima... Journal of Personalized Medicine 2021 96 not a result This is a systematic review/overview summarizing performance metrics from 22 previously published AI studies rather than producing any new AI-driven diagnostic result itself. “Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray ima...”
MGP-AttTCN: An interpretable machine learning model for the prediction of se... PLoS ONE 2021 56 assisted The abstract reports a human-designed model's benchmark accuracy on an existing task (early sepsis prediction on MIMIC-III), framing the AI as a predictor whose outputs inform clinicians and contribute to an ongoing research effort rather than the AI itself producing the paper's primary result. “when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an...”
Deep learning approaches for breast cancer detection in histopathology image... Cancer Biomarkers 2024 44 not a result This is a survey/review paper summarizing existing deep learning approaches for breast cancer detection, with no primary result produced by an AI system. “This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images.”
Structure Prediction and Computational Protein Design for Efficient Biocatal... Angewandte Chemie International Edition 2024 40 not a result This is a review/highlight commentary on the 2024 Nobel Prize and others' tools (AlphaFold, Baker's design methods) rather than a paper presenting a primary result produced by an AI system. “Here, we highlight notable contributions to the development of these computational tools and their importance for the design of functional proteins th...”
Genomic Surveillance of COVID-19 Variants With Language Models and Machine L... Frontiers in Genetics 2022 35 unsupported no abstract available for this DOI
Machine learning prediction in cardiovascular diseases: a meta-analysis Scientific Reports 2020 514 not a result This is a meta-analysis that aggregates predictive performance from prior studies rather than presenting any primary result where an AI system itself solved a task. “We aim to assess and summarize the overall predictive ability of ML algorithms in cardiovascular diseases.”
Artificial intelligence in sepsis early prediction and diagnosis using unstr... Nature Communications 2021 412 assisted The abstract reports a prediction model's accuracy (AUC 0.94) on the existing task of sepsis early warning and frames the AI's output as decision support to augment physician detection rather than autonomously completing the clinical task of diagnosing and treating sepsis. “We compare the SERA algorithm against physician predictions and show the algorithm's potential to increase the early detection of sepsis by up to 32%...”
Forecasting Renewable Energy Generation with Machine Learning and Deep Learn... Sustainability 2023 378 not a result The abstract explicitly states this is a review of existing ML/DL forecasting approaches with no new primary result produced by an AI system. “This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) a...”
De novo protein design—From new structures to programmable functions Cell 2024 344 not a result This is a perspective/review discussing the state of the field rather than presenting a primary result produced by an AI system. “In this perspective, I will discuss the state of the field of de novo protein design at the juncture of physics-based modeling approaches and AI.”
Deep-learning-enabled antibiotic discovery through molecular de-extinction Nature Biomedical Engineering 2024 189 assisted The deep-learning models only predicted candidate antimicrobial peptides, and the actual antibiotic discovery was completed by a human-run pipeline of synthesis, wet-lab validation, and mouse infection studies. “We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens.”
Risk prediction of cardiovascular disease using machine learning classifiers Open Medicine 2022 158 assisted This is a standard model-accuracy study applying existing classifiers (MLP, K-NN) to a public UCI dataset, reporting benchmark metrics rather than the AI itself solving CVD detection end to end in practice. “Experimental-based results demonstrate that a higher accuracy in detection of 82.47% and an area-under-the-curve value of 86.41% are obtained using th...”
Machine Learning in Action: Stroke Diagnosis and Outcome Prediction Frontiers in Neurology 2021 147 not a result This is a review/overview article summarizing existing machine learning studies in stroke, with no primary result produced by an AI system. “This article provides an overview of machine learning technology and a tabulated review of pertinent machine learning studies related to stroke diagno...”
Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19... European Radiology Experimental 2021 145 assisted The CNN ensemble is developed and evaluated as a decision-support tool to aid human COVID-19 diagnosis from CXR (RT-PCR remains the reference standard and the tool is still 'in training'), so the AI informs diagnosticians rather than autonomously solving the diagnostic task. “We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR)”
Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine... Drugs and Drug Candidates 2023 136 not a result This is a survey/review of virtual screening algorithms with no primary experimental result produced by any AI system. “This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug...”
CheXaid: deep learning assistance for physician diagnosis of tuberculosis us... npj Digital Medicine 2020 136 assisted The paper's primary contribution is a study of whether AI assistance improves clinician diagnostic accuracy; the clinicians made the final diagnoses, with the AI serving as a decision-support aid rather than performing the task end to end as the paper's contribution. “Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy”
AI-Driven Deep Learning Techniques in Protein Structure Prediction International Journal of Molecular Sciences 2024 136 not a result The abstract explicitly describes a review/survey of existing protein structure prediction models rather than presenting any new primary result produced by an AI system. “This review study presents a comprehensive review of the computational models used in predicting protein structure.”
MRI Deep Learning-Based Solution for Alzheimer’s Disease Prediction Journal of Personalized Medicine 2021 131 assisted The paper reports a deep-learning classifier's benchmark accuracy on an existing task (OASIS MRI diagnosis) that remains a human clinical decision, and its own conclusion frames the system as a tool for computer-assisted rather than autonomous diagnosis. “deep learning-based strategies are an effective tool for building a robust solution for Alzheimer's-assisted diagnosis based on MRI data”
Deep Learning Applications in Computed Tomography Images for Pulmonary Nodul... Diagnostics 2022 129 not a result This is a survey/review article summarizing existing deep learning literature on pulmonary nodule segmentation and classification, with no primary result produced by an AI system. “Thus, this review provides a baseline understanding of the topic for interested readers.”
Deep learning models for tuberculosis detection and infected region visualiz... Intelligent Medicine 2023 119 unsupported no abstract available for this DOI
Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Re... Journal of Clinical Medicine 2023 118 not a result This is a systematic review synthesizing prior studies of ML/DL sepsis prediction rather than presenting an AI system that itself produced a primary result. “This systematic review examined 1942 articles, selecting 42 studies while adhering to strict criteria.”
Prediction of survival and metastasis in breast cancer patients using machin... Clinical Epidemiology and Global Health 2018 116 assisted This is a human-run retrospective comparison of off-the-shelf classifiers' prediction accuracy on existing patient records, where the AI models are evaluated components rather than the source of a solved clinical problem. “This study aimed to compare the performance of six machine learning techniques two traditional methods for the prediction of BC survival and metastasi...”
An efficient mixture of deep and machine learning models for COVID-19 diagno... PLoS ONE 2020 112 assisted The AI is explicitly positioned as a computer-aided diagnosis tool providing auxiliary suggestions to human experts who make the actual diagnosis, rather than autonomously solving the diagnostic task. “can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent”
Deep Learning for Detecting and Locating Myocardial Infarction by Electrocar... Frontiers in Cardiovascular Medicine 2022 104 not a result This is a literature review surveying 59 prior deep learning studies rather than presenting a new AI-produced result. “This review aims to take stock of 59 major DL studies applied to the ECG for MI detection and localization published in recent 5 years”
Advances and challenges in automated malaria diagnosis using digital microsc... Frontiers in Microbiology 2022 87 not a result This is a review/survey of AI-based malaria diagnosis approaches that discusses advances and challenges without presenting any primary result produced by an AI system. “New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagn...”
Pre-existing and machine learning-based models for cardiovascular risk predi... Scientific Reports 2021 85 assisted The ML models only produce risk scores that inform human clinical decision-making within an existing prediction task, reporting improved accuracy metrics rather than the AI itself solving the clinical problem end to end. “The model can be easily adopted for risk assessment and clinical decision making.”
An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and... Processes 2023 74 assisted The abstract only reports accuracy metrics (MAPE, classification accuracy) of ML models predicting precipitation on an existing public weather dataset, which is a prediction component feeding a human-run flood-warning pipeline rather than an end-to-end solved flood forecasting/alert result. “We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the p...”
Comparison of risk models for mortality and cardiovascular events between ma... PLoS ONE 2019 69 assisted Machine learning served as an analytical tool inside a human-designed cohort study (Shinken Database) where researchers selected the cohort, outcomes, and comparison with logistic regression, and the abstract merely reports the developed models' AUCs on a standard risk-prediction task rather than the AI itself accomplishing the task as the paper's contribution. “The areas under the curve of the developed models for each event were 0.900, 0.912, 0.879, 0.758, and 0.753, respectively.”
Predicting sepsis onset in ICU using machine learning models: a systematic r... BMC Infectious Diseases 2023 68 not a result This is a systematic review and meta-analysis that re-evaluates prior studies' ML models rather than producing a new result or having an AI system itself solve a task. “We included 23 eligible studies with a total of 4,314,145 patients and 26 different machine learning models.”
Deep Learning-driven research for drug discovery: Tackling Malaria PLoS Computational Biology 2020 63 assisted The deep learning models performed prediction/virtual screening to prioritize compounds, but the actual discovery was completed by human-run wet-lab experimental evaluation and hit confirmation, making the AI one component in a human pipeline. “The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium fal...”
Early Prediction of Sepsis Based on Machine Learning Algorithm Computational Intelligence and Neuroscience 2021 58 assisted This is a human-run methodological study reporting accuracy (AUC) of off-the-shelf algorithms on the existing task of early sepsis prediction, with the contribution being human-designed feature-generation and imputation methods rather than an AI system that itself solved the problem. “XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979)”
Zero-Shot Clinical Trial Patient Matching with LLMs NEJM AI 2024 57 unsupported no abstract available for this DOI
Cervical cancer survival prediction by machine learning algorithms: a system... BMC Cancer 2023 57 not a result This is a systematic review synthesizing 13 previously published studies on ML-based cervical cancer survival prediction, producing no primary result of its own. “This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer.”