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
Easy-Prime: a machine learning–based prime editor design tool Genome biology 2021 68 assisted Easy-Prime is a predictive design tool whose ML output (prime editor designs) informs human researchers who then perform and validate the actual genome editing, making it a component in a human-run workflow rather than an AI-produced result. “We provide optimized PE design for installation of 89.5% of 152,351 GWAS variants.”
Optimizing fraud detection in financial transactions with machine learning a... Expert Systems 2024 66 assisted This is a human-run comparative evaluation of six standard ML models on an existing fraud-detection task, where the contribution is performance metrics and insights rather than an AI system end-to-end solving fraud detection. “This paper provides valuable insights into the efficacy of machine learning models for financial fraud detection and emphasizes the importance of stri...”
Smart Platform for Data Blood Bank Management: Forecasting Demand in Blood S... Information 2023 65 assisted The AI/ML system is explicitly a decision support tool whose forecasts and classifications inform human-run blood collection, inventory, and scheduling operations rather than autonomously producing the supply-chain outcomes. “We use machine learning and time series forecasting models to develop an AI/ML decision support system.”
Mortality Prediction of Septic Patients in the Emergency Department Based on... Journal of Clinical Medicine 2019 65 assisted The deep learning model only produces retrospective mortality risk scores to inform clinicians, with the actual clinical task of identifying and managing septic patients remaining a human-run workflow, as the abstract itself frames the system as assistance rather than an agent that completed the task. “We expect that deep learning can effectively assist medical staff in early identification of critical patients.”
De novo design of peptide binders to conformationally diverse targets with c... Science Advances 2025 64 unsupported no abstract available for this DOI
Novel antimicrobial peptide discovery using machine learning and biophysical... Drug Development Research 2019 62 assisted The machine-learning prediction was one component of a human-run pipeline: ML generated candidate 20-mers, but humans selected sequences, synthesized them, and performed the wet-lab antimicrobial/cytotoxicity/hemolysis assays that produced the actual discovery result. “Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities.”
Machine learning-based meta-analysis reveals gut microbiome alterations asso... Nature Communications 2025 61 assisted The ML models were one analytical component inside a human-designed and human-run meta-analysis, reporting classification accuracy on existing microbiome data (without cross-study generalization), while the paper's actual contribution is a human-synthesized overview of PD-microbiome associations rather than an AI-produced end-to-end result. “Within most studies, microbiome-based machine learning models could accurately classify PD patients. However, models were study-specific and did not g...”
Applications of artificial intelligence in clinical laboratory genomics American Journal of Medical Genetics Part C Seminars in Medical Genetics 2023 56 not a result This is a review/survey of AI applications in clinical laboratory genomics, describing existing and anticipated uses rather than presenting a primary result produced by an AI system. “With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to signif...”
Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme Nature Communications 2024 56 assisted The AI (MutComputeX) generated predicted enzyme variants that were then screened, validated, and completed via a human-run biosensor/wet-lab pipeline, so the improved biocatalyst is the product of the workflow rather than the AI alone. “A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltra...”
A hybrid deep learning framework for early detection of diabetic retinopathy... Scientific Reports 2025 54 assisted The paper's contribution is a human-designed hybrid CNN-RNN architecture demonstrated via benchmark classification accuracy on existing public datasets against other models, so the AI's predictions serve to evaluate a method within the human model-development workflow rather than constituting an end-to-end solved capability that is itself the paper's primary result. “Proposed model experimental evaluation demonstrate the superior performance over traditional deep learning models like CNN, RNN, InceptionV3, VGG19 an...”
Machine learning-based ABA treatment recommendation and personalization for... Brain Informatics 2022 51 assisted The ML models only generate treatment-goal recommendations that are benchmarked against and intended to inform clinician-prepared ABA plans, with the actual therapy still delivered by human practitioners in a clinical workflow. “The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81–84%, with a normalized discounted cu...”
Design of Cyclic Peptides Targeting Protein–Protein Interactions Using Alpha... International Journal of Molecular Sciences 2023 51 unsupported no abstract available for this DOI
Structure-based, deep-learning models for protein-ligand binding affinity pr... Journal of Cheminformatics 2024 49 not a result This is a review/survey of existing deep-learning affinity-prediction models that re-evaluates them for comparison, not a primary result produced by an AI system. “Here we review mainstream structure-based, deep-learning approaches for this problem... This review will potentially benefit structure-based drug disc...”
A machine learning model trained on a high-throughput antibacterial screen i... PLoS Computational Biology 2022 47 assisted The ML model serves as a compound-prioritization step within a human-run drug discovery pipeline, where researchers experimentally tested the top-ranked predictions in wet-lab growth inhibition assays to obtain the actual hits. “the developed ML approach can be used for compound prioritization before screening, increasing the typical hit rate of drug discovery”
Prediction of incident cardiovascular events using machine learning and CMR... European Radiology 2022 47 assisted This is a human-run cohort study reporting accuracy/AUC of an SVM classifier on the well-established task of cardiovascular risk prediction, with humans performing cohort selection, segmentation, and feature extraction, so the ML is a predictive component evaluating radiomics' incremental value rather than an agent that solved a problem. “To assess the model performance, the accuracy, sensitivity, specificity, and AUC were reported.”
A scoping review of artificial intelligence applications in clinical trial r... npj Digital Medicine 2025 46 not a result This is a scoping review surveying 142 existing studies rather than presenting a primary AI-produced result solving a problem. “This scoping review analyzed 142 studies published between 2013 and 2024”
Morphological profiling for drug discovery in the era of deep learning Briefings in Bioinformatics 2024 46 not a result The abstract explicitly states this is a review summarizing recent advances in morphological profiling, with no primary research result produced by an AI system. “In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling.”
Machine learning for predicting cardiac events: what does the future hold? Expert Review of Cardiovascular Therapy 2020 46 not a result This is a literature review summarizing existing work on ML-based cardiovascular risk prediction, not a primary result produced by an AI system. “In this review, the authors summarize the literature comparing the performance of machine learning methods to that of traditional, statistical-based m...”
Machine learning for optimized individual survival prediction in resectable... Journal of Cancer Research and Clinical Oncology 2022 45 assisted The paper reports comparative accuracy of ML models on the existing task of survival prediction and delivers a human-built prognostic risk-score tool intended to inform surgical oncologists' clinical judgment, making the AI a component in a human-run clinical workflow rather than a standalone AI-produced result. “we could establish a compact risk score model with 20 input parameters and thus provide a clinical tool to improve prediction of oncological outcome a...”
Improved Weather Forecasting Using Neural Network Emulation for Radiation Pa... Journal of Advances in Modeling Earth Systems 2021 44 assisted The neural network replaces only the radiation parameterization, a single component, inside the human-built, human-run operational NWP pipeline at KMA, so the forecasts and their verification are products of a human workflow the AI merely accelerates rather than an end-to-end AI-produced result. “a neural network (NN) emulator for radiation parameterization was developed to use in an operational weather forecasting model in the Korea Meteorolog...”
Machine Learning for Structural Health Monitoring of Aerospace Structures: A... Sensors 2025 44 not a result This is a survey/review paper that maps existing ML-based SHM methods and outlines future research directions, with no primary result produced by an AI system. “This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications.”
Deep learning-based framework for slide-based histopathological image analys... Scientific Reports 2022 44 assisted This is a methods paper proposing a new framework (HipoMap) and reporting its benchmark accuracy on existing, already-labeled tasks (TCGA lung cancer classification and survival analysis), so the AI's end-to-end predictions serve as validation of a technique rather than constituting the paper's contribution as a newly produced result. “HipoMap showed the Area Under the Curve (AUC) of 0.96±0.026 (5% improved) in the experiments for lung cancer classification”
Early prediction of sepsis in intensive care patients using the machine lear... Journal of Critical Care 2024 43 unsupported no abstract available for this DOI
Classification of retinal images with deep learning for early detection of d... Signal Processing and Communications Applications Conference (SIU) 2018 43 unsupported no abstract available for this DOI
Machine Learning for Accelerating Energy Materials Discovery: Bridging Quant... Advanced Energy Materials 2025 43 not a result The abstract surveys existing ML paradigms, applications, challenges, and emerging directions for energy materials discovery without reporting any primary result produced by an AI system. “Current challenges include data quality, extrapolation to new chemical spaces, and physical interpretability. Emerging solutions involve equivariant a...”
Application of Deep Learning in Histopathology Images of Breast Cancer: A Re... Micromachines 2022 42 not a result This is a literature review summarizing 107 prior studies on deep learning in breast cancer histopathology, with no primary result produced by an AI system. “After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three direct...”
Power System Transient Stability Assessment Based on Machine Learning Algori... Mathematics 2023 42 assisted The ML models are classification components developed and evaluated by humans within an emergency-control design workflow, with the abstract reporting benchmark accuracy on a test system (IEEE39) rather than the AI itself performing stability assessment or control end to end as the paper's solved result. “Algorithms were tested using the test power system IEEE39. In the case of the test sample, accuracy of instability classification for XGBoost was 91.5...”
A deep learning‐based approach for software vulnerability detection using co... IET Software 2022 42 unsupported no abstract available for this DOI
Evaluating GPCR modeling and docking strategies in the era of deep learning-... Computational and Structural Biotechnology Journal 2022 42 unsupported The supplied abstract contains only the text 'Graphical abstract' with no substantive content, so no claim about AI's role can be made. “Graphical abstract”
Deep Learning-Based Attack Detection and Classification in Android Devices Electronics 2023 41 assisted The abstract reports a human-developed model's benchmark accuracy (precision, recall, efficiency) on existing malware datasets within a human-run pipeline of dataset creation, training, and evaluation, rather than an AI system itself producing the detection result end to end as the paper's contribution. “The effectiveness of our proposed model is validated using well-established datasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017.”
Advancements in Artificial Intelligence in Noninvasive Cardiac Imaging: A Co... Clinical Cardiology 2025 40 not a result This is a comprehensive review surveying AI applications in cardiac imaging modalities, presenting no primary result or AI-solved task of its own. “AI is increasingly applied across various imaging modalities, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT),...”
Bone mineral density response prediction following osteoporosis treatment us... Scientific Reports 2021 39 assisted The AI is explicitly a decision-support system that predicts BMD response to inform clinician treatment choices, with the 9.54% gain being a retrospective simulation rather than AI-driven treatment outcomes, so it aids rather than solves the personalized therapy task. “our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and perso...”
Peptide-based drug design using generative AI Chemical Communications 2025 38 not a result The abstract explicitly states this is a review of recent progress in AI-driven peptide drug design, with no primary result produced by an AI system. “Here, we review recent progress in peptide-based drug design using AI, focusing on generative architectures and interactions.”
Diagnostic assessment of deep learning for melanocytic lesions using whole-s... Translational Oncology 2021 37 assisted The abstract explicitly positions the deep learning algorithm as a supplemental, pre-screening aid for pathologists rather than an agent that itself delivered the diagnostic outcome as the paper's contribution. “Deep learning algorithm might function as a supplemental tool to assist pathologist by automatically pre-screening and highlighting interest regions p...”
Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Lea... Applied Sciences 2020 36 assisted The system is explicitly framed as a computer-aided diagnosis tool whose classification output supports dermatologists, and the paper reports benchmark accuracy (ISIC-2019) for a model operating as one component in a human clinical workflow rather than the AI itself producing a standalone solved result. “Computer-aided diagnosis is a helpful tool to help dermatologists in the detection of these kinds of illnesses.”
Intracranial Hemorrhage Detection in CT Scans using Deep Learning IEEE International Conference on Big Data Computing Service and Applications (BigDataService) 2020 36 assisted The model is explicitly framed as a tool to assist radiologists and accelerate their human-run diagnostic workflow, reporting benchmark accuracy on an existing RSNA dataset rather than the AI completing the clinical task itself. “To assist with this process, a deep learning model can be used to accelerate the time it takes to identify them.”
Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous... Studies in health technology and informatics 2021 35 assisted The paper presents a prediction pipeline whose output is explicitly framed as clinical decision support for human diagnosticians, with the contribution being benchmarked accuracy on existing retinal datasets rather than the AI itself performing diagnosis end-to-end. “Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis.”
Genomic Prediction of Wheat Grain Yield Using Machine Learning Agriculture 2022 35 assisted This is a comparative evaluation of existing algorithms' prediction accuracy on an existing task (genomic prediction of wheat grain yield), where the models inform breeders rather than the paper's contribution being an AI-produced result. “we compared ML and deep learning (DL) algorithms with classical Bayesian approaches, across a range of different FS methods, for their performance in...”
Prediction of evolutionary constraint by genomic annotations improves functi... Genome biology 2022 34 assisted The deep learning model predicts nucleotide conservation, but that prediction is one annotation component feeding a human-run pipeline of genomic prediction, experimental validation, and variant prioritization for breeding, rather than the AI itself producing the end result. “it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of...”
Antibiotic discovery with artificial intelligence for the treatment of Acine... mSystems 2024 33 assisted The AI (QSAR/virtual screening) only ranked candidate compounds, and the actual antibacterial result was produced and confirmed by human-run wet-lab assays (microdilution, time-kill, mutant studies), making AI one component in a human-completed discovery pipeline. “To validate these findings, one lead compound exhibiting promising binding stability as well as favorable pharmacokinetics properties, namely demethox...”