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.
| Paper | Year | Cited | Why it is held |
|---|---|---|---|
| Matrix Factorization-based Technique for Drug Repurposing Predictions | 2020 | 56 | assisted The paper's contribution is an enhanced prediction method whose outputs are putative drug-target/disease candidates evaluated via retrospective accuracy metrics and literature checks, i.e., a prediction component feeding a human-driven drug-repurposing pipeline rather than AI-produced, validated repurposing results. “obtaining very good performance, with an Average Precision Score of 0.82” |
| Deep Learning for SARS COV-2 Genome Sequences | 2021 | 56 | assisted The abstract reports a proposed CNN-Bi-LSTM model's classification accuracy on genome-sequence tasks, positioning deep learning as a tool ('alternative avenue') to inform virus and motif detection rather than the AI itself producing a definitive solved result. “These results are encouraging enough to recognise deep learning algorithms as alternative avenues for detecting SARS CoV-2 as well as detecting regula...” |
| Predicting High-Risk Prostate Cancer Using Machine Learning Methods | 2019 | 55 | assisted This is a human-run retrospective evaluation of standard ML algorithms' accuracy on an existing clinical prediction task, where the model's risk predictions serve as decision support for the human screening workflow rather than being themselves the AI-produced result. “We evaluated the accuracy of various machine learning algorithms in predicting high-risk prostate cancer.” |
| Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery | 2024 | 54 | not a result This is a survey/review book chapter describing deep learning pipelines for virtual screening rather than reporting a primary result produced by an AI system. “This chapter explores the systematic deep learning pipeline for virtual screening, covering data acquisition, molecular representation learning, model...” |
| Deep learning for protein complex structure prediction | 2023 | 52 | not a result The abstract explicitly describes a review summarizing existing structure-prediction methods and limitations, with no primary result produced by an AI system. “This review highlights the foundation for current accurate structure prediction of protein complexes and possible ways to address the remaining limita...” |
| Deep and Transfer Learning Approaches for Automated Early Detection of Monke... | 2023 | 47 | assisted The paper reports benchmark-style accuracy of existing architectures on a public skin-lesion dataset, framing the models as aids that 'could help' human mpox diagnosis rather than the AI itself completing the diagnostic task end to end. “It was concluded that CNN, VGG19, and Inception v3 could help in early detection of mpox skin lesions, and Inception v3 returned the best (96.56%) cla...” |
| Identification of Novel Antibacterials Using Machine Learning Techniques | 2019 | 47 | assisted The ML models only scored/prioritized candidate molecules, and the actual identification and confirmation of antibacterials was completed by human-run wet-lab validation (biological studies, MIC/CC50, in vitro and in vivo inhibitor identification), making AI one component in a human pipeline. “The antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study.” |
| Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Ho... | 2024 | 46 | not a result This is a systematic review summarizing existing literature on AI applications for HAI surveillance and diagnosis, not a primary result produced by an AI system. “This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potential...” |
| A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using His... | 2023 | 44 | assisted This is a human-run comparative evaluation of existing pre-trained models on an existing histopathology dataset, reporting accuracy metrics rather than contributing a new end-to-end capability produced by the AI. “we have analyzed pre-trained deep transfer learning models such as ResNet50, ResNetl01, VGG16, and VGG19 for detecting breast cancer using the 2453 hi...” |
| Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Co... | 2023 | 44 | assisted The ML models only prioritized candidate compounds within a human-run pipeline: humans synthesized the 54 new complexes and performed the wet-lab antibacterial assays that produced the improved hit-rate, so the AI informed rather than produced the discovery. “The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7%) against methicillin-r...” |
| Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine... | 2021 | 44 | assisted The paper reports the accuracy of a predictive scoring model (AUROC 0.83) as a clinical decision-support capability within a human-run diagnostic workflow, not an AI system that itself performs or completes sepsis recognition and management end to end. “The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83.” |
| Using Machine Learning to Predict Synergistic Antimalarial Compound Combinat... | 2018 | 42 | assisted CoSynE's predictions served as candidate prioritization within a human-run wet-lab screening pipeline, the synergistic combinations were established by experimental validation, not by the model itself, and the abstract frames the AI's value as reducing experimental effort by 41%. “CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70...” |
| An Artificial Intelligence Analysis of Electrocardiograms for the Clinical D... | 2024 | 42 | not a result This is a narrative review summarizing existing AI-ECG applications and their limitations, with no new primary result produced by an AI system. “An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review” |
| Integrative Analysis of Next-Generation Sequencing for Next-Generation Cance... | 2021 | 41 | not a result This is a review/survey of machine learning and systems biology approaches for NGS cancer data, with no primary result produced by any AI system. “In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems bi...” |
| A New Approach to Drug Repurposing with Two-Stage Prediction, Machine Learni... | 2022 | 40 | assisted The ML pipeline only generated candidate drug lists whose efficacy is predicted but not demonstrated, and the abstract explicitly frames the output as informing future human-driven drug discovery and trials rather than solving repurposing itself. “These were previously noted to be effective against two of the three diseases, and have a high probability of being effective against the other.” |
| Classification and deep-learning–based prediction of Alzheimer disease subty... | 2023 | 37 | assisted The deep neural network is a prediction model reporting accuracy within a larger human-driven GWAS study whose primary contributions (subtype identification and kidney-function insight) came from human analysis, not the AI. “We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort...” |
| A Systematic Review of Deep Learning Methodologies Used in the Drug Discover... | 2023 | 35 | not a result This is a systematic review summarizing prior deep learning studies in drug discovery, not a primary result produced by an AI system. “This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further i...” |
| Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeatin... | 2020 | 34 | assisted The ML algorithm identified candidate fragments, but the result was produced through a human-run pipeline combining theory, computation, and wet-lab experiments to validate the fragments and confirm permeation of selected compounds, making the AI one component rather than the agent that solved the problem. “By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting co...” |
| Machine Learning Models for Early Prediction of Sepsis on Large Healthcare D... | 2022 | 34 | assisted This is an experimental benchmark study evaluating ML models' predictive accuracy against existing scoring systems on a retrospective public dataset (MIMIC-III), not an AI system solving sepsis detection end to end. “This work presents an experimental study of some machine-learning-based models for sepsis prediction” |
| Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFol... | 2024 | 34 | not a result The abstract explicitly states the paper reviews recent studies rather than presenting a primary result produced by an AI system. “We review recent studies that use structure-prediction neural networks to design proteins” |
| Deep Machine Learning for Computer-Aided Drug Design | 2022 | 27 | not a result This is a review/perspective highlighting existing deep learning advances in computer-aided drug design rather than reporting a primary result produced by an AI system. “These include advances in both small molecular and macromolecular modeling, as highlighted herein.” |
| Applications of machine learning in computer-aided drug discovery | 2022 | 27 | not a result This is a review article summarizing deep learning trends in structure-based drug design, with no primary research result produced by an AI system. “This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and b...” |
| The Large Scale Machine Learning in an Artificial Society: Prediction of the... | 2015 | 26 | assisted Machine learning is only one component used to optimize individual behaviors within a human-built artificial society simulation, while the epidemic prediction and intervention conclusions come from the human-designed computational experiment pipeline. “artificial Beijing is reconstructed based on geodemographics and machine learning is involved to optimize individuals' behaviors” |
| Computer Vision Malaria Diagnostic Systems—Progress and Prospects | 2017 | 26 | not a result This is a review/survey of computer vision malaria diagnostic platforms, discussing progress and barriers rather than presenting a primary result produced by an AI system. “In this review, we discuss the most advanced computer vision malaria diagnostic technologies and investigate several of their features which are centr...” |
| Machine Learning in Carbon Capture, Utilization, Storage, and Transportation... | 2025 | 25 | not a result This is a survey/review paper synthesizing existing ML applications in CCUS, with no primary result produced by an AI system. “This review addresses this gap by systematically evaluating ML applications across all major CCUS components” |
| Recent Applications of Deep Learning Methods on Evolution- and Contact-Based... | 2021 | 25 | not a result This is a review/survey of existing deep learning methods in protein structure prediction, with no primary result produced by an AI system. “Here, we explore the recent applications of deep learning methods in the protein structure prediction area.” |
| Machine Learning Models for Cardiovascular Disease Events Prediction | 2022 | 22 | assisted The paper reports a comparative accuracy benchmark of standard ML models on an existing cohort prediction task, with the outputs intended to inform a future human-run clinical risk-estimation pipeline (TIMELY) rather than the AI itself producing the solved clinical outcome. “These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.” |
| Diabetes Prediction Through Linkage of Causal Discovery and Inference Model... | 2025 | 10 | assisted The paper applies standard ML models to an existing public Kaggle dataset and reports their accuracy alongside causal-inference analysis, making AI one component in a human-run analytical workflow rather than the producer of a novel end-to-end result. “The study achieved high accuracy across models using logistic regression (84.84%) and deep learning (84.83%).” |
| Faculty Opinions recommendation of A deep learning approach to antibiotic di... | 2020 | not a result The item is a Faculty Opinions recommendation, an expert commentary recommending someone else's paper, so it contains no primary result of its own; the supplied abstract is merely the recommended article's abstract, not a result produced by this publication. | |
| Large language models encode clinical knowledge | 2023 | 3,955 | assisted This is a benchmark/evaluation study where Flan-PaLM's accuracy on existing medical QA datasets is measured within a human-run evaluation framework, and the authors themselves conclude the best model remains inferior to clinicians, so the clinical task is not solved by the AI. “The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians.” |
| A review of the application of machine learning in water quality evaluation | 2022 | 793 | not a result This is a review paper surveying existing applications of machine learning to water quality evaluation, with no primary result produced by an AI system. “In this review, we describe the cases in which machine learning algorithms have been applied to evaluate the water quality in different water environm...” |
| CADD-Splice—improving genome-wide variant effect prediction using deep learn... | 2021 | 637 | assisted The deep-learning splice scores are pre-existing component predictions that the authors themselves compared, selected, and integrated into their own CADD pipeline, with the humans running the integration and evaluation rather than the AI producing the end result. “We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genom...” |
| Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction | 2017 | 549 | unsupported no abstract available for this DOI |
| Prediction of In‐hospital Mortality in Emergency Department Patients With Se... | 2015 | 491 | unsupported no abstract available for this DOI |
| Efficient evolution of human antibodies from general protein language models | 2023 | 449 | assisted The language model only suggested mutations that guided a human-run wet-lab pipeline of screening and laboratory evolution, which is what actually produced the improved antibodies. “We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of...” |
| Deep Learning Image Reconstruction for CT: Technical Principles and Clinical... | 2023 | 426 | not a result The abstract explicitly describes a review article surveying deep learning CT reconstruction principles and applications, with no primary result produced by an AI system. “This review presents an overview of the principles, technical approaches, and clinical applications of DLR, including metal artifact reduction algorit...” |
| Almanac — Retrieval-Augmented Language Models for Clinical Medicine | 2024 | 401 | assisted The AI generates responses that a human expert panel evaluates in a human-run benchmark workflow, and the paper itself frames the system as having 'potential' to aid clinicians in decision-making rather than producing an AI-solved clinical result. “Our results show the potential for LLMs with access to domain-specific corpora to be effective in clinical decision-making.” |
| Improving de novo protein binder design with deep learning | 2023 | 396 | assisted The deep learning models (AlphaFold2/RoseTTAFold as in-silico design assessors, ProteinMPNN as a sequence designer) are components augmenting a pre-existing, human-run energy-based design pipeline that humans completed with wet-lab validation, rather than an AI agent end-to-end producing the binders. “Here, we explore the augmentation of energy-based protein binder design using deep learning.” |
| Advances in De Novo Drug Design: From Conventional to Machine Learning Metho... | 2021 | 352 | not a result This is a review article summarizing existing de novo drug design methods rather than presenting a primary result produced by an AI system. “This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and hig...” |
| Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning | 2020 | 305 | assisted The machine learning models serve as regression tools inside a human-designed remote-sensing pipeline, and the paper's contribution is an assessment of data-fusion strategies and a comparison of model accuracies for estimating crop traits, not an AI system performing crop monitoring end to end. “The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring usin...” |