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
Formation of a Lightweight, Deep Learning-Based Weed Detection System for a... Applied Sciences 2023 41 assisted The AI is a weed-detection component inside a human-built laser weeding robot pipeline, and the abstract only reports benchmark metrics (mAP, FPS) plus a calculated field-time estimate rather than the AI itself performing the weeding task end to end. “This work focuses on developing a lightweight weed detection mechanism to assist laser weeding robots.”
Learning to predict RNA sequence expressions from whole slide images with ap... Communications Biology 2023 40 assisted The paper proposes a model whose RNA-seq predictions are benchmarked against state-of-the-art and explicitly framed as a tool to assist downstream computational pathology workflows (search and classification), not as an AI-produced result that itself constitutes the paper's finding. “The proposed tRNAsformer can assist as a computational pathology tool to facilitate a new generation of search and classification methods by combining...”
NEURD offers automated proofreading and feature extraction for connectomics Nature 2025 40 not a result The paper's contribution is a software package/toolkit whose stated purpose is to enable and facilitate downstream analyses by researchers, not to report a primary result the AI itself produced. “These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neu...”
Machine Learning-Guided Optimization of p -Coumaric Acid Production in Yeast ACS Synthetic Biology 2024 40 unsupported no abstract available for this DOI
Benchmark analysis of day-ahead solar power forecasting techniques using wea... Conference record of the IEEE Photovoltaic Specialists Conference 2019 40 assisted The paper is a benchmark evaluation reporting accuracy/skill scores of existing ML techniques on the established task of day-ahead PV forecasting, with the contribution being the comparative analysis rather than a new end-to-end capability. “With a skill score of respectively 41.4% and 41.3% Gradient Boosting and Random Forests are found to outperform the other models.”
3Cnet: pathogenicity prediction of human variants using multitask learning w... Bioinformatics 2021 39 assisted 3Cnet is a pathogenicity-prediction tool whose predictions serve as input to a human-run clinical diagnostic workflow (the paper frames its value as improving diagnosis rates), rather than the AI itself producing the diagnostic result. “more effectively discovering pathogenic variants and thereby improving diagnosis rates”
Machine Learning Explainability in Breast Cancer Survival Studies in health technology and informatics 2020 38 assisted The ML survival model is a component in a human-driven workflow whose actual contribution is the authors' comparison of LIME and SHAP explanations, not an AI-produced result standing as the paper's primary outcome. “we used Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to interpret the model's predictions”
Deep learning for dual detection of microsatellite instability and POLE muta... npj Precision Oncology 2024 38 assisted The AI's image-based predictions are explicitly positioned as a preliminary screening step whose flags inform a clinical workflow that humans still complete via confirmatory molecular testing, and the POLE result is a 75% screening flag rather than an end-to-end detection. “deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in...”
Deep learning for prediction of energy consumption: an applied use case in a... Applied Intelligence 2024 37 assisted The paper's contribution is a comparative accuracy evaluation of existing neural architectures on a prediction task that is explicitly framed as one step inside a human-driven energy-management workflow, not an AI end-to-end solving of the problem. “performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption”
A Systematic Review and Meta-Analysis of Applying Deep Learning in the Predi... Translational Vision Science & Technology 2023 36 not a result This is a systematic review and meta-analysis synthesizing prior studies of deep learning models; the paper's contribution is the literature synthesis itself, not any AI system producing a new result. “The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict...”
Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorpora... Interdisciplinary Sciences Computational Life Sciences 2023 36 assisted The abstract only reports a new model's comparative benchmark accuracy (MSE/AUPR) on the existing drug-target interaction prediction task, using expert-derived binding-site features, with predictions meant to inform human drug-discovery workflows rather than the AI itself producing the solved result. “The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods.”
A novel approach for skin lesion symmetry classification with a deep learnin... Computers in Biology and Medicine 2022 35 assisted The paper's contribution is a human-designed and human-evaluated CNN classification pipeline whose symmetry predictions are meant to inform dermatologists' lesion assessment, with the result being a benchmark F1-score on a human-labeled dataset rather than an AI-produced outcome. “we present a new simple, robust and fast classification pipeline that outperforms methods based on traditional approaches or pre-trained networks, wit...”
A machine learning approach for multimodal data fusion for survival predicti... npj Precision Oncology 2025 35 assisted The paper's contribution is a reusable comparison pipeline and benchmark-style accuracy findings on an existing task (survival prediction on public TCGA data), with ML models as evaluated components rather than an AI-produced solution to the problem itself. “The study provides a reusable pipeline for the research community, suggesting future work on larger cohorts.”
A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters... mBio 2022 35 assisted The machine learning models serve as measurement instruments within a human-designed comparative study; the paper's contribution is the meta-finding about optimal taxonomic resolution, not an AI-produced detection capability, making this a benchmark-style analysis where AI informed the human researchers' conclusion. “To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated t...”
Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future... ACS Catalysis 2025 35 not a result This is a review/survey summarizing existing ML models and case studies in enzyme mining, with no primary result produced by an AI system. “This review provides a systematic overview of state-of-the-art machine learning models and highlights representative case studies that demonstrate the...”
Defect Detection and Monitoring in Metal Additive Manufactured Parts through... Smart and Sustainable Manufacturing Systems 2018 34 unsupported no abstract available for this DOI
Interpretable machine learning for prediction of clinical outcomes in acute... Frontiers in Neurology 2023 33 assisted This is a routine clinical prediction model paper: the ML system reports accuracy (AUC) on an existing prognostication task using retrospective registry data, and its output is designed to inform human clinicians who make the actual treatment decisions, so the AI is a decision-support component rather than the agent that solved a problem. “Predicting the prognosis of acute ischemic stroke (AIS) is crucial in a clinical setting for establishing suitable treatment plans.”
Classifying and diagnosing Alzheimer’s disease with deep learning using 6735... Scientific Reports 2025 33 assisted The paper reports accuracy metrics of four off-the-shelf pretrained CNN architectures fine-tuned on an existing public MRI dataset, which is benchmark-style performance reporting on an existing task rather than the AI itself solving Alzheimer's diagnosis end to end. “In this study, the Alzheimer MRI Preprocessed Dataset was used, which includes 6735 brain structural MRI scan images. After data preprocessing and nor...”
Best practices for developing microbiome-based disease diagnostic classifier... Gut Microbes 2025 33 not a result This is a benchmarking/best-practices study that re-runs and compares 156 existing tool-parameter-algorithm combinations to derive recommendations and a guideline for building classifiers, rather than an AI system producing a primary solved result. “can provide a comprehensive guideline for constructing diagnostic models for a range of diseases”
Multivariate Analysis and Machine Learning in Cerebral Palsy Research Frontiers in Neurology 2017 32 not a result This is a survey/review paper summarizing prior multivariate and machine learning studies in cerebral palsy research, with no primary result produced by any AI system. “This paper aims to identify such multivariate studies and provide an overview of this relatively young field.”
Ensemble-Based Machine Learning Algorithm for Loan Default Risk Prediction Mathematics 2024 31 assisted This is a human-run comparative benchmark study applying off-the-shelf ML models and resampling techniques to an existing task (LendingClub default prediction), reporting accuracy metrics rather than an AI system that itself solved a problem end to end. “Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%”
A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle B... Diagnostics 2022 30 assisted The model reports classification accuracy (ROC-AUC) as a screening aid explicitly framed to assist pathologists in the diagnostic workflow, rather than producing a standalone diagnostic result end-to-end. “Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs)...”
Prediction and risk assessment of sepsis-associated encephalopathy in ICU ba... Scientific Reports 2022 30 assisted The ML model is a clinical prediction tool whose output informs physicians' SAE risk assessment rather than performing the diagnostic task end-to-end, with physicians explicitly evaluating and using the model's predictions. “The interpretable model was effectively evaluated by professional physicians and can help them predict the occurrence of SAE more intuitively.”
Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classif... Computational and Mathematical Methods in Medicine 2021 30 assisted The abstract explicitly frames the model as a second-opinion CAD tool assisting medical experts, and the paper's contribution is a training hyperparameter (dynamic learning rate) demonstrated via accuracy on an existing classification task, not an AI end-to-end solving of metastasis detection. “Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation.”
Credit risk prediction based on causal machine learning: Bayesian network le... Journal of Forecasting 2024 30 assisted The paper reports a Bayesian network model's predictive accuracy on six existing labeled credit datasets as a comparative method evaluation, and explicitly frames its output as decision support informing human risk judgments rather than as an AI-produced primary result. “This analysis provides decision‐makers with the necessary tools to make informed judgments about the risk profile of borrowers.”
Weed Detection from Unmanned Aerial Vehicle Imagery Using Deep Learning—A Co... Sensors 2024 29 assisted The paper's contribution is a low-cost multispectral camera hardware comparison, with an off-the-shelf U-Net serving as the evaluation instrument inside a human-run pipeline (sensor building, data collection, thresholding/annotation, training, evaluation), so the AI informed rather than itself constituted the solved problem. “This research paper introduces a self-built, low-cost, multispectral camera system and evaluates it against the high-end MicaSense Altum system.”
Evaluating Diagnostic Accuracy and Treatment Efficacy in Mental Health: A Co... European Journal of Investigation in Health Psychology and Education 2025 29 assisted This is an evaluation study benchmarking existing commercial LLMs' diagnostic accuracy against professional norms, and its own conclusion frames the models as supportive tools requiring human oversight rather than systems that solved the clinical task. “LLMs provide valuable support in diagnostics and treatment planning but cannot replace professional discretion.”
Interpretable machine learning for materials discovery: Predicting CO2 adsor... APL Materials 2024 28 assisted The ML model improves prediction accuracy and interpretability of CO2 adsorption on an existing hypothetical MOF dataset, serving as a screening guide within a human-run discovery pipeline rather than itself discovering or validating a new material. “Overall, this study provides a novel perspective to guide the initial high-throughput screening of MOFs as high-performance CO2 adsorption materials.”
A Hybrid Semi-Automated Workflow for Systematic and Literature Review Proces... Future Internet 2024 28 assisted The LLM only summarizes and extracts information that a researcher then uses to make inclusion/exclusion decisions in an explicitly semi-automated, human-in-the-loop workflow, so the AI is a component in a human-run process rather than the agent producing the review outcome. “This is then used by a researcher to make inclusion/exclusion decisions quickly.”
Data-driven strategies for the computational design of enzyme thermal stabil... Acta Biochimica et Biophysica Sinica 2023 28 not a result This is a review/perspective summarizing datasets, methods, and future directions rather than reporting a primary result produced by an AI system. “In this review, we propose a data-driven architecture for enzyme thermostability engineering and summarize some widely adopted datasets, as well as ma...”
Improving Precipitation Forecasts with Convolutional Neural Networks Weather and Forecasting 2023 26 unsupported no abstract available for this DOI
Validating the accuracy of deep learning for the diagnosis of pneumonia on c... European Radiology Experimental 2024 25 assisted This is a human-run validation study in which a pre-existing CNN's CXR predictions were benchmarked against a reference diagnosis produced by a multidisciplinary expert panel; the paper's contribution is the evaluation finding (that AI accuracy was previously overestimated), not an AI-produced solution to the diagnostic problem. “we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis”
A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Ima... Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) 2020 24 assisted The CNN is presented as a diagnostic aid to 'reduce the burden from doctors,' reporting benchmark classification accuracy on an existing dataset (BreakHis) rather than the AI itself producing an end-to-end cancer detection result. “To enhance efficiency of BC i.e. Breast Cancer detection using histopathology images and also to reduce the burden from doctors, we design a deep lear...”
Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A S... Frontiers in Public Health 2022 23 not a result This is a systematic literature review surveying existing arbovirus prediction models, with no primary result produced by any AI system. “In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dyn...”
Performance of Machine Learning for Tissue Outcome Prediction in Acute Ische... Frontiers in Neurology 2022 23 not a result This is a systematic review and meta-analysis that synthesizes prior studies' ML performance rather than producing any new AI-driven result itself. “This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction fro...”
Monitoring hydropower reliability in Malawi with satellite data and machine... Environmental Research Letters 2020 23 assisted The random forest regression is one component inside a human-designed framework; its discharge predictions feed into a human-run assessment of hydropower reliability rather than the AI itself producing the paper's primary result. “The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urb...”
A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Au... Energies 2023 23 assisted The DRL is only the high-level tactical layer of a human-designed hybrid architecture whose low-level control is handled by model-based NMPC, and the whole system is validated only in simulation rather than the AI itself being the end-to-end driving capability that constitutes the paper's result. “the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while...”
Identifying Regulatory Elements via Deep Learning Annual Review of Biomedical Data Science 2020 23 not a result This is a survey/review of deep learning methods in regulatory genomics, with no primary result produced by an AI system. “In this review, we survey the successes of deep learning in the field of regulatory genomics.”
Implementation of large language models in electronic health records PLOS Digital Health 2025 22 assisted The paper reports a deployment and adoption study of an LLM assistant embedded in clinicians' workflows (summarization, retrieval, note drafting) where the AI's output supports humans who still perform the clinical work, rather than the AI itself producing the primary result. “The most common tasks were summarization, information retrieval, and note drafting, which together accounted for over 70% of interactions.”
Comparing two deep learning sequence-based models for protein-protein intera... arXiv (Cornell University) 2019 22 unsupported no abstract available for this DOI