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 |
|---|---|---|---|
| Machine learning applied to retinal image processing for glaucoma detection:... | 2020 | 138 | not a result This is a systematic review summarizing prior ML studies on glaucoma detection, with no primary result produced by an AI system. “This is a systematic review on the main algorithms using machine learning (ML) in retinal image processing for glaucoma diagnosis and detection.” |
| Deep-Learning-Based Image Reconstruction and Enhancement in Optical Microsco... | 2019 | 136 | not a result The abstract explicitly states this is an overview/survey of recent deep learning work in computational microscopy, not a primary research result. “This article provides an overview of some of the recent work using deep neural networks to advance computational microscopy and sensing systems, also...” |
| Accelerated enzyme engineering by machine-learning guided cell-free expressi... | 2025 | 134 | assisted The ML models are one component of a human-run platform (cell-free assembly, expression, functional assays) that predicts variants to guide enzyme engineering rather than producing the engineered enzymes end-to-end. “We developed a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapid...” |
| A Survey of Learning-based Automated Program Repair | 2023 | 132 | unsupported no abstract available for this DOI |
| Protein Function Prediction: From Traditional Classifier to Deep Learning | 2019 | 132 | not a result This is a commentary/review summarizing Zhang et al.'s DeepFunc work rather than presenting a primary result of its own. “In article number 1900019, Issue 12, Zhang et al. construct DeepFunc, a deep learning framework using derived feature information of protein sequence...” |
| Large language models for structured reporting in radiology: past, present,... | 2024 | 129 | not a result This is a narrative review surveying existing literature on LLMs for structured reporting, with no primary result produced by an AI system. “this narrative review aims to provide an overview of LLMs for SR in radiology and beyond” |
| A deep learning, image based approach for automated diagnosis for inflammato... | 2020 | 126 | assisted The abstract reports a CNN classifier's accuracy on the existing human-performed task of diagnosing inflammatory skin diseases and explicitly frames the model as a 'diagnosis assistant' to help clinicians, so the AI informs clinical diagnosis rather than autonomously producing the solved result. “we developed an artificial intelligence dermatology diagnosis assistant (AIDDA)” |
| CXNet-m1: Anomaly Detection on Chest X-Rays With Image-Based Deep Learning | 2018 | 122 | assisted This is a methods paper reporting a new CNN architecture's benchmark metrics on the existing ChestX-ray14 task, positioning the model as a diagnostic aid to improve human work efficiency and diagnosis accuracy rather than autonomously solving the clinical problem. “The experimental results show that our light model CXNet-m1 with sin-loss function achieves better accuracy rate, recall rate, F1-score, and AUC value...” |
| Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes? | 2019 | 122 | unsupported no abstract available for this DOI |
| DeepBacs for multi-task bacterial image analysis using open-source deep lear... | 2022 | 119 | not a result The paper is a toolkit/guide with a training database that enables users to apply deep learning to bacterial imaging, rather than reporting a primary solved result. “DeepBacs guides users without expertise in machine learning methods to leverage state-of-the-art artificial neural networks to analyse bacterial micro...” |
| Machine learning of material properties: Predictive and interpretable multil... | 2022 | 114 | assisted The ML models are re-run on existing benchmark datasets to report accuracy comparable to prior kernel/neural approaches, with the actual scientific insights derived by humans interpreting model coefficients, so the AI is a component in a human-driven methodological demonstration rather than the producer of a new result. “We revisit material datasets used in several works and demonstrate that simple linear combinations of nonlinear basis functions can be created, which...” |
| Evaluation of Surgical Skills during Robotic Surgery by Deep Learning-Based... | 2020 | 107 | not a result the paper is a study ("evaluation of…"): it measures or reviews work rather than producing a result for an AI to have solved |
| An Internet of Things System for Underground Mine Air Quality Pollutant Pred... | 2018 | 104 | assisted The ANN in Azure ML is one component of a human-designed and human-operated IoT monitoring pipeline (sensors, PCA, base station), providing MEI predictions that inform mine safety rather than the AI itself being the paper's end-to-end solved task. “The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI.” |
| Comparative validation of machine learning algorithms for surgical workflow... | 2023 | 100 | not a result This is a benchmark/dataset paper whose contribution is the HeiChole dataset and comparative evaluation of 12 external teams' algorithms, not an AI system producing a primary result. “This novel HeiChole benchmark can be used for comparable evaluation and validation of future work.” |
| Benchmarking deep learning splice prediction tools using functional splice a... | 2021 | 100 | not a result This is a benchmarking study that re-runs and evaluates existing splice prediction tools against functional assay data rather than producing a new AI-driven result. “We benchmarked established and deep learning splice prediction tools on published gold standard sets of 71 NCSS and 81 DI variants” |
| Machine learning-guided acyl-ACP reductase engineering for improved in vivo... | 2021 | 96 | assisted The ML system guided sequence design within an iterative human-run design-test-learn pipeline where researchers performed the wet-lab construction, screening, and characterization to ultimately produce the improved enzymes, so the AI was a component rather than the agent that produced the result. “Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences...” |
| Deep learning model-assisted detection of kidney stones on computed tomograp... | 2022 | 94 | assisted This is a retrospective diagnostic-accuracy study evaluating a deep learning model's performance metrics against radiologist readings on an existing clinical task, not a demonstration of the model autonomously solving the clinical problem end to end. “The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined.” |
| COBAYN | 2016 | 92 | unsupported no abstract available for this DOI |
| Generalizable sgRNA design for improved CRISPR/Cas9 editing efficiency | 2020 | 89 | assisted The ML algorithm is a prediction/design tool within the larger human-run CRISPR editing workflow; humans performed the wet-lab plasmid library experiments and the actual genome editing, with the AI only scoring sgRNA candidates. “Differential sequence and structural features among the most and least potent sgRNAs were then used to train a machine learning algorithm for assay de...” |
| Machine learning models for predicting post-cystectomy recurrence and surviv... | 2019 | 83 | assisted The ML models report prediction accuracy (>70% sensitivity/specificity) as a benchmark within a human-driven clinical workflow, with outputs intended to inform clinicians' monitoring and treatment decisions rather than the AI autonomously performing the clinical task end to end. “Such predictions may inform patient monitoring schedules and post-cystectomy treatments.” |
| Re-Routing Drugs to Blood Brain Barrier: A Comprehensive Analysis of Machine... | 2022 | 82 | assisted The paper's contribution is a comparative performance analysis of existing ML models on existing BBB datasets, with the resulting permeability predictions only informing human drug-repurposing decisions rather than the AI accomplishing repurposing end to end. “This study comprehensively analyzes the performance of the well-known machine learning models on two different datasets to overcome dataset-related bi...” |
| Artificial Intelligence–Driven Ultra-Fast Superresolution MRI | 2022 | 81 | not a result The abstract explicitly states this is a review article covering concepts, commercial solutions, and future directions rather than presenting a primary AI-produced result. “In this article, we review basic concepts of deep learning–based acquisition and image reconstruction techniques for accelerating and improving the qu...” |
| Cardiovascular complications in a diabetes prediction model using machine le... | 2023 | 80 | not a result This is a systematic review/survey of existing ML prediction models for CVD in T2DM patients, assessing others' work via PROBAST and TRIPOD rather than producing a new AI-driven result. “This review is to investigate the current development of prediction model for the risk of cardiovascular disease (CVD) among type 2 diabetes (T2DM) pa...” |
| Measuring the model risk-adjusted performance of machine learning algorithms... | 2022 | 77 | assisted The paper's contribution is a human-designed risk-adjustment framework for evaluating ML models; NLP and the ML models are components used within that human-run workflow rather than an AI system producing the primary result itself. “we compute the weight of each category according to the intensity of their mentions, using natural language processing and a risk terminology based on...” |
| Multi-omics approaches to studying gastrointestinal microbiome in the contex... | 2024 | 77 | not a result The abstract explicitly describes itself as a mini-review surveying existing multi-omics and machine learning approaches to the gut microbiome, with no primary result produced by any AI system. “In this mini-review, our primary focus is metagenomics” |
| Tailoring industrial enzymes for thermostability and activity evolution by t... | 2025 | 75 | assisted The machine learning model serves as a predictive component within the human-driven iCASE enzyme-engineering strategy, informing variant design that humans then construct and validate experimentally across four enzyme types, rather than the AI itself producing the improved enzymes end-to-end. “this dynamic response predictive model using structure-based supervised machine learning is established to predict enzyme function and fitness” |
| Forecasting severe grape downy mildew attacks using machine learning | 2020 | 68 | assisted The ML models produce risk forecasts as one component of a human-run epidemiological workflow whose actual goal, reducing fungicide treatments via decision rules, is a projected human management outcome informed by the AI's predictions, not a result the AI itself delivered. “Our results also show that the use of fungicide treatment decision rules that take into account local characteristics would reduce the number of treat...” |
| LLM-assisted systematic review of large language models in clinical medicine | 2026 | 67 | not a result This is a systematic review/survey synthesizing existing literature, a category that fails regardless of how much AI was used in the curation workflow. “This LLM-assisted review identified 4,609 peer-reviewed studies in clinical medicine between January 2022 and September 2025” |
| Distilling large language models for matching patients to clinical trials | 2024 | 67 | unsupported no abstract available for this DOI |
| Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning | 2020 | 66 | assisted The paper reports a model's classification accuracy (82%) on an existing human-labeled dataset and explicitly frames the AI as a decision-support tool for human diagnosticians rather than as the agent that solved the diagnostic problem. “Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.” |
| Human vs. machine: Detecting wildlife in camera trap images | 2022 | 64 | unsupported no abstract available for this DOI |
| Forecasting the <i>Dst</i> index usi... | 2017 | 62 | unsupported no abstract available for this DOI |
| A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin L... | 2022 | 61 | assisted The paper's contribution is a plug-and-play data augmentation technique validated through benchmark classification metrics on existing datasets, with the AI's outputs explicitly framed as informing clinicians in a screening workflow rather than the AI itself solving the clinical problem end to end. “which can be combined with any model through a plug-and-play mode” |
| A machine learning approach for individual claims reserving in insurance | 2019 | 60 | unsupported no abstract available for this DOI |
| Testing the reliability of an AI-based large language model to extract ecolo... | 2024 | 52 | assisted This is a human-designed evaluation benchmarking an LLM's extraction speed and accuracy against a human gold-standard reviewer, and the authors themselves conclude the AI's output still requires human quality assurance to complete the task, making the LLM a component in a human-run workflow rather than the agent that solved it. “additional quality assurance steps are required to ensure data integrity” |
| Antibody design using deep learning: from sequence and structure design to a... | 2024 | 50 | not a result The abstract explicitly identifies the paper as a survey reviewing existing advancements in deep learning for antibody design, with no primary result of its own. “This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies.” |
| DeepInteract: Deep Neural Network Based Protein-Protein Interaction Predicti... | 2017 | 50 | unsupported no abstract available for this DOI |
| War and Deforestation: Using Remote Sensing and Machine Learning to Identify... | 2023 | 49 | assisted The LandTrendr algorithm and Tasseled Cap transformation were processing steps inside a human-designed remote sensing study; the researchers ran the workflow, interpreted the drivers of deforestation, and drew the policy conclusions, so AI/ML was a component rather than the agent that produced the result. “Spectral information was synthetized using the Tasseled Cap transformation and the time series data was simplified and despiked using the LandTrendr a...” |
| Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation... | 2024 | 49 | unsupported no abstract available for this DOI |
| Skin Lesion Classification using Deep Learning Architectures | 2020 | 49 | assisted The deep learning models report classification accuracy on an existing task and are explicitly framed as a pre-diagnosis aid whose output informs doctors and patients, who make the actual medical decision. “This can give the patients as well as doctors a good idea of whether or not there is a need for medical attention and can avoid unnecessary panic/fals...” |