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
Machine learning identifies candidates for drug repurposing in Alzheimer’s d... Nature Communications 2021 293 assisted DRIAD produces a ranked nomination of candidate drugs within a human-run discovery pipeline (human cell-culture perturbations feed it, and its output requires further human validation and trials), so the AI informed rather than solved drug repurposing. “We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomar...”
Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: P... IEEE Access 2021 293 not a result This is a survey reviewing 147 existing articles on brain disease diagnosis, with no primary result produced by any AI system. “In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases”
Matching patients to clinical trials with large language models Nature Communications 2024 283 assisted TrialGPT produces eligibility predictions and rankings that inform human screeners who still complete the recruitment workflow, as evidenced by the user study framing the system as reducing human screening time rather than performing recruitment itself, and its 87.3% criterion-level accuracy (below expert performance) is a prediction evaluated inside a human pipeline. “our user study reveals that TrialGPT can reduce the screening time by 42.6% in patient recruitment”
InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learnin... Journal of Medicinal Chemistry 2021 261 assisted This is a methods paper reporting a new predictive model's benchmark accuracy on existing tasks (affinity prediction, virtual screening, pose scoring) against baselines, functioning as a scoring component for human-run drug design pipelines rather than an AI agent producing a novel end-to-end scientific result. “Extensive binding affinity prediction, large-scale structure-based virtual screening, and pose prediction experiments demonstrated that IGN achieved b...”
A foundation model for clinician-centered drug repurposing Nature Medicine 2024 239 assisted TxGNN produces ranked drug-repurposing predictions validated retrospectively against clinician off-label use, with outputs explicitly framed as candidates for human expert investigation rather than the AI itself establishing any new therapeutic use. “TxGNN's drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop inte...”
AMPlify: attentive deep learning model for discovery of novel antimicrobial... BMC Genomics 2022 221 assisted AMPlify's role was to prioritize candidate peptide sequences, but the actual discovery, confirming antimicrobial activity, was completed by human-run wet-lab bioactivity assays, making this a prediction component inside a larger human pipeline. “demonstrate its utility in prioritizing peptide sequences derived from the Rana [Lithobates] catesbeiana (bullfrog) genome”
Deep Learning for Lung Cancer Nodules Detection and Classification in CT Sca... AI 2020 202 not a result This is a survey/review paper that summarizes and compares existing deep learning CAD systems for lung nodule detection rather than producing a new result itself. “In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection.”
MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and I... Genome Medicine 2022 190 assisted The deep learning models are presented as decision-support tools that 'help' humans identify and prioritize variants and whose scores feed downstream human genotype-phenotype analyses, i.e., benchmarked accuracy on an existing prediction task within a human interpretation workflow rather than the AI itself delivering a solved result. “we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (...”
Rapid protein stability prediction using deep learning representations eLife 2023 174 unsupported no abstract available for this DOI
Deep learning suggests that gene expression is encoded in all parts of a co-... Nature Communications 2020 172 assisted The deep learning model predicts mRNA abundance from sequence, but the paper's actual contribution, the discovery of regulatory rules, motif interactions, and co-evolution, was made by humans analyzing the model's outputs within a human-run scientific pipeline, not produced directly by the AI. “By searching for DNA regulatory motifs across the gene regulatory structure, we discover that motif interactions could explain the whole dynamic range...”
Application of Machine Learning in Material Synthesis and Property Predictio... Materials 2023 165 not a result The abstract explicitly describes a review/survey of machine learning principles and applications in materials science, with no primary result produced by any AI system. “In this review, the basic principles of machine learning are briefly outlined.”
Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopatho... IEEE Journal of Biomedical and Health Informatics 2020 157 not a result This is a challenge review paper that summarizes and evaluates methods submitted by others rather than presenting an AI system whose own result is the paper's contribution. “This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation.”
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery... Cell Systems 2020 153 assisted The AI model's uncertainty-guided predictions served to prioritize compounds within a human-run pipeline where the actual discovery (nanomolar kinase affinity, M. tuberculosis growth inhibition) was established by human experimental validation, not by the AI itself. “identifying and experimentally validating compounds with nanomolar affinity for diverse kinases and whole-cell growth inhibition of Mycobacterium tube...”
Prediction of dengue outbreak in Selangor Malaysia using machine learning te... Scientific Reports 2021 142 assisted This is a model-comparison study reporting accuracy metrics for predicting dengue outbreaks, where the predictions are intended to inform health officials who would take action, rather than an AI system end-to-end solving the outbreak problem. “five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine le...”
Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Re... Journal of Imaging 2023 136 not a result This is a survey/review paper that critically evaluates existing deep-learning research rather than producing a new AI-driven result. “This work reviews the most common retinal pathologies, provides an overview of prevalent imaging modalities and presents a critical evaluation of curr...”
Random forest-based prediction of stroke outcome Scientific Reports 2021 130 assisted The abstract reports a Random Forest model's predictive accuracy (AUC) on the existing clinical task of stroke-outcome prognostication, framing the model as a tool that 'can be effectively used' within a human-run cohort study to inform clinical assessment, rather than an AI autonomously producing a standalone result. “machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity”
Improving deep learning protein monomer and complex structure prediction usi... Nature Methods 2024 130 assisted DeepMSA2 is an MSA-construction pipeline whose output feeds existing deep learning structure predictors; it improves those predictors' inputs rather than itself performing the structure prediction end to end. “optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predic...”
Computational Tools and Resources for CRISPR/Cas Genome Editing Genomics Proteomics & Bioinformatics 2022 129 not a result This is a review/survey of existing computational tools and workflows for CRISPR sgRNA design, with no primary result produced by any AI system. “In this review, we first briefly introduce the different features of CRISPR/Cas tools that should be taken into consideration to achieve specific purp...”
Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinic... JCO Clinical Cancer Informatics 2020 127 assisted The paper evaluates an AI matching tool's agreement, sensitivity, and specificity against manual review within a human-run screening workflow, with the abstract itself framing the output as 'system-assisted' determinations rather than the AI autonomously performing trial screening. “System-assisted trial eligibility determinations were substantially faster than manual review, and the system reliably excluded ineligible patients fo...”
Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ven... JACC. Cardiovascular imaging 2021 124 unsupported no abstract available for this DOI
Machine Learning and Prediction of Infectious Diseases: A Systematic Review Machine Learning and Knowledge Extraction 2023 124 not a result This is a systematic review synthesizing 75 existing studies on ML-based infectious disease prediction, with no primary AI-produced result of its own. “At the end of the search, this systematic review contained 75 records.”
Alzheimer’s disease diagnosis and classification using deep learning techniq... PeerJ Computer Science 2022 122 assisted The abstract merely reports benchmark accuracies of existing CNN architectures (DenseNet-169, ResNet-50) on an Alzheimer's classification dataset, positioning the model as a proposed diagnostic aid for error-prone manual diagnosis rather than an AI that itself solved the clinical task end to end. “The proposed model is usable for real-time analysis and classification of Alzheimer's disease.”
Machine learning-guided discovery of ionic polymer electrolytes for lithium... Nature Communications 2023 115 assisted The ML workflow only recommended candidate ionic liquids; humans then selected, synthesized, and electrochemically tested the actual electrolytes and cells, so the AI prediction was one component inside a human-run experimental pipeline. “By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a lithium salt, we develop a series of thin (~50 μm) and r...”
Deep generative modeling for protein design Current Opinion in Structural Biology 2021 115 not a result This is a review surveying five classes of generative models and providing a framework, with no primary result produced by an AI system itself. “In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model gui...”
Machine learning-powered antibiotics phenotypic drug discovery Scientific Reports 2019 111 assisted The machine learning is one component of a human-run phenotypic screening pipeline whose output (MoA predictions) informs human chemists guiding SAR optimization, rather than the AI itself producing the discovery end to end. “machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA)”
A Survey on Deep-Learning-Based Diabetic Retinopathy Classification Diagnostics 2023 110 not a result This is a survey paper that reviews and compares existing deep-learning methods for diabetic retinopathy classification rather than producing any new result itself. “this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description...”
Pediatric Severe Sepsis Prediction Using Machine Learning Frontiers in Pediatrics 2019 110 assisted The paper reports a predictive model's retrospective accuracy (AUROC) on an existing clinical prediction task as a decision-support tool to inform clinicians, not an AI end-to-end solving sepsis detection in practice. “the machine learning algorithm achieved an AUROC of 0.916 for discrimination between severe sepsis and control pediatric patients at the time of onset...”
Machine Learning and Deep Learning Approaches in Breast Cancer Survival Pred... Folia Biologica 2019 108 assisted The paper reports comparative accuracies of standard off-the-shelf classifiers on an existing prediction task (breast cancer survival from registry data), with the predictions ultimately serving to inform human treatment-protocol decisions rather than the AI itself constituting the solved contribution. “The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respecti...”
Weed Detection Using Deep Learning: A Systematic Literature Review Sensors 2023 106 not a result This is a systematic literature review synthesizing prior work on weed detection rather than presenting a new AI-produced result. “In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection.”
Machine learning to predict cardiovascular risk International Journal of Clinical Practice 2019 104 assisted The paper evaluates and compares the predictive performance of existing ML methods against risk scales within a human-run cohort analysis, rather than the AI itself producing the end-to-end result as the contribution. “To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk s...”
An overview and metanalysis of machine and deep learning-based CRISPR gRNA d... RNA Biology 2019 102 not a result This is a review/survey paper summarizing and comparing existing ML/DL-based gRNA design tools rather than presenting any new AI-produced result. “In this review, we present an overview and comparative analysis of machine and deep learning (MDL)-based algorithms”
Analysis of missense variants in the human genome reveals widespread gene-sp... The American Journal of Human Genetics 2022 89 assisted The machine learning approach and random-forest MutScore were components wielded by human researchers in a human-driven analysis workflow, and the paper reports a predictor's accuracy on the existing task of variant pathogenicity prediction to aid (not replace) human interpretation of variants of uncertain significance. “We used a machine learning approach to analyze the within-gene distribution of missense variants observed in hereditary conditions and cancer.”
DeepDRK: a deep learning framework for drug repurposing through kernel-based... Briefings in Bioinformatics 2021 85 assisted DeepDRK is a predictive model whose outputs (drug-response scores and repurposing candidates) serve to prioritize drugs for downstream human validation in a precision-medicine workflow, rather than the AI itself completing drug repurposing end to end. “offering an alternative way to prioritize repurposing drugs in precision cancer treatment”
Integrating central nervous system metagenomics and host response for diagno... Nature Communications 2022 82 assisted The machine learning classifier is one component of a combined diagnostic workflow (alongside wet-lab mNGS sequencing run by humans), and the reported result is the accuracy of that integrated assay rather than the AI performing diagnosis end to end. “By leveraging the specificity of mNGS and the sensitivity of an MLC created from CSF host transcriptomes, the combined assay has high sensitivity (88....”
Automated detection of cardiovascular disease by electrocardiogram signal an... Cardiovascular Diagnosis and Therapy 2020 81 assisted The CNN's ECG classifications are benchmarked against human expert labels on an existing diagnostic task, and the paper explicitly frames the model as a decision-support aid whose output informs human clinicians rather than an agent that autonomously completed the clinical diagnostic problem. “The proposed CNN model could help reduce misdiagnosis and missed diagnosis in primary care settings and also improve efficiency and save manpower cost...”
External validation of a deep learning electrocardiogram algorithm to detect... International Journal of Cardiology 2021 80 assisted This is a human-designed and human-run external validation study that reports the accuracy (AUC, sensitivity, specificity) of a previously derived algorithm on an existing detection task, so the AI is the component being evaluated rather than the agent producing the paper's result. “We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population.”
Early Prediction of Mortality, Severity, and Length of Stay in the Intensive... Frontiers in Medicine 2021 75 assisted The ML models produce retrospective risk predictions whose stated role is to inform clinicians' treatment and resource decisions, making the AI a decision-support component in a human-run clinical workflow rather than the agent that solved the clinical problem. “can provide a comprehensive early warning of sepsis, which will contribute to the formulation and management of clinical decisions and the allocation...”
On classifying sepsis heterogeneity in the ICU: insight using machine learni... Journal of the American Medical Informatics Association 2019 73 assisted The ML models are experimental instruments in a human-driven study whose contribution is the insight that stratifying sepsis subpopulations improves feature selection, not an AI system solving sepsis prediction end to end. “features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients r...”
Knowledge-driven drug repurposing using a comprehensive drug knowledge graph Health Informatics Journal 2020 73 assisted The ML system produces repurposing candidate predictions validated retrospectively against known diabetes treatments, with actual repurposing decisions and case-study investigation left to human researchers, making this a prediction component in a larger human workflow. “The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only usin...”
The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on... Frontiers in Immunology 2021 71 assisted The paper's primary contribution is the human-driven identification and validation of immune subtypes via clustering on transcriptomic data, with the deep learning model serving only as a downstream component that predicts those human-defined subtypes from pathology images. “Finally, deep learning can predict the immune subtypes well.”