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
Deep learning analysis of electrocardiogram for risk prediction of drug-indu... European Heart Journal 2021 83 unsupported no abstract available for this DOI
Machine learning-guided co-optimization of fitness and diversity facilitates... Nature Communications 2024 83 assisted MODIFY's ML predictions and library designs guided a human-run wet-lab directed evolution workflow to produce the biocatalysts, rather than the AI itself performing the enzyme engineering end to end. “Using MODIFY, we engineer generalist biocatalysts derived from a thermostable cytochrome c to achieve enantioselective C-B and C-Si bond formation”
Artificial Intelligence Scribe and Large Language Model Technology in Health... Plastic & Reconstructive Surgery Global Open 2025 82 not a result This is a review/commentary summarizing advantages, limitations, and recommendations for AI scribe technology, with no primary result produced by an AI system. “Medical AI developers should use robust regulatory oversights, adhere to ethical guidelines, correct bias in algorithms, and improve detection and cor...”
DeepPVP: phenotype-based prioritization of causative variants using deep lea... BMC Bioinformatics 2019 78 assisted The abstract reports a deep-learning tool's comparative benchmark accuracy on the existing prediction task of variant prioritization, where the ranked 'likely causative' variants are candidate outputs that inform human geneticists in a diagnostic workflow rather than an AI-produced end result. “We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features.”
Data augmentation importance for classification of skin lesions via deep lea... Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) 2018 77 unsupported no abstract available for this DOI
A review of the applications of multi-agent reinforcement learning in smart... Frontiers in Robotics and AI 2022 76 not a result This is a literature review that surveys and maps existing MARL applications to smart factory attributes rather than producing a new AI-driven result. “In this research, we will review the literature on the applications of MARL to tasks within a smart factory and then demonstrate a mapping connecting...”
Artificial intelligence for optimizing recruitment and retention in clinical... Journal of the American Medical Informatics Association 2024 76 not a result This is a scoping review that maps and summarizes existing literature on AI for clinical trial recruitment, producing no primary result of its own. “The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of...”
Assessment of deep learning assistance for the pathological diagnosis of gas... Modern Pathology 2022 76 assisted The study evaluates deep learning as a decision-support tool for human pathologists who made the diagnoses themselves, with the AI only informing their reads rather than producing the diagnostic result. “Our results demonstrated that DL assistance indeed improved pathologists' accuracy and efficiency in gastric cancer diagnosis”
Machine learning in biohydrogen production: a review Biofuel Research Journal 2023 76 not a result This is a review paper surveying machine learning applications in biohydrogen production, with no primary result produced by an AI system. “This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production.”
DockStream: a docking wrapper to enhance de novo molecular design Journal of Cheminformatics 2021 76 not a result The paper's contribution is a software wrapper integrating existing docking backends into a generative design platform, i.e., a toolkit that enables work rather than a primary scientific result produced by an AI system. “DockStream is a flexible, stand-alone molecular docking wrapper that provides access to a collection of ligand embedders and docking backends.”
DeepWAS: Multivariate genotype-phenotype associations by directly integratin... PLoS Computational Biology 2020 74 assisted The deep learning component only supplies per-variant regulatory effect predictions that are fed as features into a human-designed multivariate GWAS pipeline, so the AI is one input component rather than the agent producing the disease associations. “We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting.”
Crystal twins: self-supervised learning for crystalline material property pr... npj Computational Materials 2022 72 assisted The paper's contribution is a human-designed self-supervised method whose value is demonstrated as improved prediction accuracy on existing benchmarks, making the GNN a prediction component in an established evaluation task rather than the agent that solved a materials problem. “we significantly improve the performance of GNN on 14 challenging material property prediction benchmarks”
Deep learning for inferring transcription factor binding sites Current Opinion in Systems Biology 2020 70 not a result This is a review/survey paper that highlights recent advances and discusses future directions rather than presenting a primary research result. “Here we highlight timely advances in deep learning for genomics... We describe recent applications, model architectures, and advances in local and glo...”
CGBVS‐DNN: Prediction of Compound‐protein Interactions Based on Deep Learnin... Molecular Informatics 2016 70 unsupported no abstract available for this DOI
Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Ve... Transportation Science 2024 69 unsupported no abstract available for this DOI
Use of artificial intelligence for cancer clinical trial enrollment: a syste... JNCI Journal of the National Cancer Institute 2023 64 not a result This is a systematic review and meta-analysis synthesizing prior studies of AI trial-enrollment tools rather than an AI system producing a new primary result itself. “Articles were included if they reported on AI actively being used in the clinical trial enrolment process.”
Deep learning enhancing guide RNA design for CRISPR/Cas12a‐based diagnostics iMeta 2024 63 assisted The deep learning model predicts and ranks crRNA candidates, but the effective diagnostic reagents are established through human-run wet-lab CRISPR assays, making the AI a prediction component inside a human-completed assay-design and validation workflow rather than the agent that produced the end result. “all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform cou...”
Convolution Neural Networks and Self-Attention Learners for Alzheimer Dement... Sensors 2023 62 assisted The paper is a human-designed and human-run comparative benchmark of existing off-the-shelf architectures on public datasets, where the researchers evaluate model accuracies rather than the AI itself producing the paper's primary result. “The contribution of this research was to investigate for the first time, to our knowledge, three current and promising deep convolutional models (ResN...”
Lung Cancer Survival Prediction via Machine Learning Regression, Classificat... IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2018 61 assisted The paper's primary contribution is a human-designed evaluation reporting accuracy metrics of standard off-the-shelf models (Random Forests, GBM, linear regression) on the existing task of SEER-based survival prediction, rather than an AI system whose own predictive output constitutes the solved result. “The regression results show that RF had the best performance for survival times ≤6 and >24 months (RMSE 10.52 and 20.51, respectively), while GBM perf...”
Determination of disease severity in COVID-19 patients using deep learning i... Diagnostic and Interventional Radiology 2020 61 assisted The primary results (clinical associations with opacity severity) came from human radiologist review, and the deep learning model's role was limited to demonstrating feasibility of opacity detection, validated against human interpretation with only moderate concordance (kappa 0.51-0.71), rather than itself producing the severity determinations that constitute the paper's contribution. “In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial o...”
Application of machine learning and genetic optimization algorithms for mode... PLoS ONE 2021 59 assisted The ML/GA system produced a yield-prediction model and a suggested ideotype, but the abstract frames these outputs as decision support for human breeders who will actually select parents and make crosses, making the AI a component in a human-run breeding pipeline rather than the agent that solved the problem. “which can be used for selecting parental lines and designing promising crosses for developing cultivars with improved genetic yield potential”
Application of Machine Learning Models for Survival Prognosis in Breast Canc... Information 2019 56 assisted The paper applies standard existing ML regression models to report prediction accuracy on breast cancer survival data within a human-run clinical workflow intended to inform therapy and management decisions, rather than the AI itself producing the end result as the paper's core contribution. “the applied machine learning methods provide promising results in terms of accuracy of survival time prediction”
Deep Learning for Concrete Crack Detection and Measurement Metrology 2024 55 assisted The deep learning models serve as classification/segmentation components within a human-engineered three-stage pipeline, and the paper's novel contribution (millimetre crack measurement) comes from the non-AI laser calibration method, with accuracies reported on existing crack detection tasks. “The final stage involved measuring crack width in millimetres by using a novel laser calibration method.”
A Deep Learning–Enabled Electrocardiogram Model for the Identification of a... Canadian Journal of Cardiology 2021 55 unsupported no abstract available for this DOI
Deep learning-based image segmentation for defect detection in additive manu... The International Journal of Advanced Manufacturing Technology 2024 53 not a result This is a survey/overview paper reviewing existing deep learning methods for AM defect detection, with no primary result produced by an AI system. “This paper presents an overview of computer vision-assisted patch-wise defect localization and pixel-wise segmentation methods reported for AM process...”
Machine learning-based prediction of survival prognosis in cervical cancer BMC Bioinformatics 2021 53 assisted Humans designed and ran the entire TCGA analysis pipeline (preprocessing, Cox feature selection, K-means clustering, model construction) to produce a prognostic stratification tool whose survival predictions exist to inform clinical management, so the ML was the analytical method in a human-driven study rather than an agent that itself produced a standalone result. “The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model.”
Deep learning analysis of resting electrocardiograms for the detection of my... European Heart Journal - Digital Health 2021 52 not a result This is a systematic review synthesizing 12 previously published studies rather than producing a new primary result, so it fails regardless of the AI content of the reviewed papers. “The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac p...”
Interpretable machine learning model for early prediction of 28-day mortalit... European journal of medical research 2024 52 assisted This is a development-and-validation study of a clinical risk prediction model whose output is intended to inform clinicians' decisions rather than the AI itself performing or completing the clinical task end to end. “The model established will have the potential to improve the level of clinical practice”
The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide... PLoS ONE 2013 52 assisted Machine learning was used as an exploratory analytical tool inside a human-designed and human-executed study (researchers collected 1632 isolates, performed all phenotypic MIC characterization, and drew the scientific conclusions from the ML outputs), rather than an AI system itself producing the paper's result end to end. “Therefore, machine learning techniques were applied to explore the data.”
Deep learning for protein secondary structure prediction: Pre and post-Alpha... Computational and Structural Biotechnology Journal 2022 50 unsupported The supplied abstract contains only the placeholder text 'Graphical abstract' with no substantive content, so no claim about AI solving or assisting can be made. “Graphical abstract”
CONNECT: a neural network based framework for emulating cosmological observa... Journal of Cosmology and Astroparticle Physics 2023 49 assisted The neural network is an emulation component (a plug-in) inside a human-orchestrated MontePython inference pipeline, humans generate the CLASS training data, design the training, and run the sampling, so the AI accelerates a workflow humans still run rather than producing the scientific result end-to-end. “a neural network framework emulating class computations as an easy-to-use plug-in for the popular sampler MontePython”
Unlocking HDR-mediated nucleotide editing by identifying high-efficiency tar... Scientific Reports 2019 48 assisted The machine learning model predicts high-efficiency target sites and ssODN designs to guide researchers, but the actual HDR-mediated nucleotide editing is performed by humans in wet-lab experiments, making the AI a design-informing component of a human-run pipeline. “we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy”
Detecting cardiomyopathies in pregnancy and the postpartum period with an el... European Heart Journal - Digital Health 2021 47 assisted The paper reports retrospective accuracy metrics (AUC, sensitivity, specificity) for a deep learning model positioned as a screening aid whose outputs would inform clinicians, with definitive diagnosis grounded in echocardiography, a model evaluation inside a human clinical pipeline rather than the AI itself solving the diagnostic task. “with the potential to become a powerful initial screening tool for cardiomyopathy in the obstetric care setting”
Polymer design via SHAP and Bayesian machine learning optimizes pDNA and CRI... Chemical Science 2024 47 assisted The ML (SHAP interpretation and Bayesian optimization) is one component in a human-run wet-lab workflow of polymer synthesis, characterization, and in vivo validation, informing rather than itself producing the delivery result. “Our facile coupling of synthesis, characterization, and machine analysis provides powerful tools to quantitate performance parameters accelerating nex...”
Deep structured learning for variant prioritization in Mendelian diseases Nature Communications 2023 43 assisted MAVERICK is a pathogenicity-prediction tool benchmarked on already-solved patient cohorts, and its real-world impact is described as 'facilitating' human identification of a novel disease gene, meaning its predictions informed humans who completed the diagnostic/gene-discovery workflow rather than the AI itself producing the discovery. “MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease.”
Determining a minimum set of variables for machine learning cardiovascular e... Cardiovascular Research 2021 42 assisted The paper reports ML model accuracy on an existing task (MACE risk stratification from SPECT MPI) with the actual contribution being a human-driven feature-reduction analysis to make the model practical for physicians, so the AI is a component informing a clinical workflow rather than the agent whose output constitutes the contribution. “ML models with fewer collected variables may be more practical for clinical implementation.”
Optimal tuning of three deep learning methods with signal processing and ano... Structural Health Monitoring 2024 42 assisted The deep learning models serve as classifiers inside a human-engineered pipeline (MEMD/Wavelet feature extraction, meta-heuristic tuning), and the contribution is reported classification accuracies on bridge monitoring data rather than the AI autonomously performing damage assessment end to end. “The average accuracy values are obtained as 98.13, 97.96, and 97.79 for 1D-CNN, LSTM, and MLP, respectively.”
Learning deep representations of enzyme thermal adaptation Protein Science 2022 42 assisted The paper's contribution is a prediction model benchmarked on existing tasks (predicting optimal catalytic and melting temperatures), with the actual engineering of thermostable enzymes left as future human work the model merely 'guides'. “DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.”
A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery Journal of Chemical Information and Modeling 2020 41 assisted DESMILES is a generative design tool whose reported results are failure-rate metrics on a previously published benchmark and computational docking scores; it performs only the design step of the human-run drug-discovery cycle with no experimentally validated discovery, so the AI augments rather than solves the problem. “The use of deep learning technologies could augment the typical practice of using human intuition in the design cycle, and thereby expedite drug disco...”
Uncertainty estimation for deep learning-based automated analysis of 12-lead... European Heart Journal - Digital Health 2021 41 assisted The paper's contribution is a methodological evaluation of uncertainty-estimation techniques around a DNN ECG classifier within a human-in-the-loop workflow where uncertain cases are deferred to physicians, not the AI end-to-end solving the diagnostic task. “The clinical simulation showed that the accuracy of the algorithm increased as uncertain predictions were referred to the physician.”