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
Machine learning models for prediction of adverse events after percutaneous... Scientific Reports 2022 48 assisted The paper reports comparative accuracy metrics of ML prediction models against existing risk scores to inform clinical decision-making, rather than an AI system performing the task end-to-end as the contribution. “To determine whether machine learning (ML) techniques predict peri-PCI adverse events ... with better discrimination or calibration than the National...”
Evaluating clinical AI summaries with large language models as judges npj Digital Medicine 2025 47 not a result the paper is a study ("evaluating…"): it measures or reviews work rather than producing a result for an AI to have solved
A novel strategy for classifying the output from an in silicovaccine discove... BMC Bioinformatics 2013 46 assisted The ML system is a filtering/classification step within a human-run vaccine discovery pipeline, and the abstract explicitly states that true validation of candidates can only happen in the laboratory, so the AI informs rather than solves the problem. “Vaccine candidates from an in silico approach can only be truly validated in a laboratory.”
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of... Metabolites 2024 45 not a result This is a review paper surveying AI-based approaches to enzyme, substrate interaction prediction, with no primary result produced by an AI system. “This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, chall...”
Deep learning in macroscopic diffuse optical imaging Journal of Biomedical Optics 2022 45 not a result This is a review/survey paper summarizing prior deep learning work in diffuse optical imaging, with no primary result produced by an AI system. “We aim to comprehensively review the use of DL applied to macroscopic diffuse optical imaging (DOI).”
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos Proceedings - IEEE International Conference on Robotics and Automation 2020 43 assisted The paper's contribution is a human-designed representation-learning algorithm (Motion2Vec) whose merit is reported as benchmark accuracy on the existing JIGSAWS task against state-of-the-art baselines, rather than an AI-produced result that itself constitutes the contribution. “Results give 85.5% segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose i...”
Effectiveness of Transfer Learning for Deep Learning-Based Electrocardiogram... Healthcare Informatics Research 2021 43 assisted The paper's contribution is a human-designed methodological evaluation showing that transfer learning improves ECG classifier F1-scores under data scarcity, with the deep learning models serving as instruments in a comparison experiment rather than being the agent whose output constitutes the result. “Transfer learning effectively overcomes the data shortages that can compromise ECG domain analysis by deep learning.”
A Survey of Automatic Speech Recognition for Dysarthric Speech Electronics 2023 43 not a result This is a scoping review/survey of prior ASR research for dysarthric speech, not a primary result produced by an AI system. “Our research aims to provide a scoping review of ASR for dysarthric speech, covering papers in this field from 1990 to 2022.”
APOGEE 2: multi-layer machine-learning model for the interpretable predictio... Nature Communications 2023 42 assisted APOGEE 2 is a pathogenicity-scoring tool whose precomputed predictions serve as one input into the human-run ACMG/AMP clinical interpretation workflow, rather than an AI-produced result that itself constitutes the paper's primary outcome. “With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact.”
Protein–Protein Interactions Prediction via Multimodal Deep Polynomial Netwo... IEEE Journal of Biomedical and Health Informatics 2018 42 assisted The paper's contribution is a human-designed classification method (multimodal DPN plus regularized extreme learning machine) whose performance is benchmarked on existing public PPI datasets, so the AI's outputs are accuracy evaluations on known data rather than an end-to-end AI-produced scientific result. “The proposed method is tested on the public dataset of H. pylori, Human, and Yeast and achieves average accuracies of 97.87%, 99.90%, and 98.11%, resp...”
Inferring single-cell transcriptomic dynamics with structured latent gene ex... Cell Reports Methods 2023 42 unsupported no abstract available for this DOI
Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak U... Applied System Innovation 2021 41 unsupported no abstract available for this DOI
A machine learning approach for wind turbine power forecasting for maintenan... Energy Informatics 2025 41 assisted The paper's contribution is a comparison of nine existing ML models' forecasting accuracy on an existing dataset, with the forecasts serving as input to human-driven maintenance planning rather than the AI performing the task end to end. “This study presents and compares nine machine learning (ML) methods for forecasting... Computational results show that the GNN-based forecasting model...”
Therapeutic enzyme engineering using a generative neural network Scientific Reports 2022 40 assisted The generative model is one component of a combined computational-and-wet-lab engineering pipeline: it designs candidate sequences that are then constructed and experimentally validated, so the AI informed rather than itself produced the improved enzyme variants. “combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic...”
CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-... Electronics 2019 39 assisted The system only predicts sgRNA cleavage efficiency as a support tool within a human-driven design workflow, with its contribution being a correlation metric on existing datasets rather than an end-to-end accomplishment. “CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency.”
Surgical instrument detection and tracking technologies: Automating dataset... Frontiers in Robotics and AI 2022 38 not a result The abstract explicitly describes a review/overview of existing detection and tracking technologies rather than presenting a new AI-produced result. “This review encapsulates the current trends in artificial intelligence (AI) based visual detection and tracking technologies for surgical instruments...”
Deep learning of gene relationships from single cell time-course expression... Briefings in Bioinformatics 2021 38 unsupported no abstract available for this DOI
mAML: an automated machine learning pipeline with a microbiome repository fo... Database 2020 38 not a result This is a toolkit and database description paper presenting an AutoML pipeline and a microbiome repository that enable future work rather than solving a specific scientific problem, with benchmark performance only demonstrating the tool's functionality. “The mAML pipeline and the GMrepo ML repository are expected to be important resources for researches in microbiology and algorithm developments.”
Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery arXiv (Cornell University) 2016 37 unsupported no abstract available for this DOI
The impact of chemotherapy and survival prediction by machine learning in ea... BMC Geriatrics 2022 37 assisted The machine learning models are survival-prediction components within a human-run epidemiological workflow (propensity matching, multivariable survival analysis) whose output informs chemotherapy treatment decisions rather than the AI itself producing the clinical result. “The LightGBM is a trustful model to predict the survival and provide precious systemic treatment for patients received chemotherapy.”
CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythm... IEEE Signal Processing Letters 2021 35 assisted This is a methods paper whose contribution is a human-designed contrastive pre-training framework, evaluated by reporting comparative accuracy on the existing task of ECG arrhythmia classification, rather than the AI itself producing a novel solved result. “Experimental results show that the proposed CLECG strategy outperforms other self-supervised methods and supervised transfer learning strategies.”
Artificial intelligence in knee osteoarthritis: A comprehensive review for 2... Osteoarthritis Imaging 2023 34 not a result This is a literature review surveying existing 2022 publications on AI for knee osteoarthritis, with no primary AI-produced result of its own. “The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artifici...”
Detection of sarcopenic obesity and prediction of long‐term survival in pati... Journal of Surgical Oncology 2021 34 assisted The paper's contribution is a clinical assessment of prevalence and prognostic value of sarcopenic obesity in gastric cancer patients, with machine learning serving as a component tool within a human-run prognostic study rather than being the end-to-end solved task. “we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of gastric cancer patients using preoperative CT”
Large Language Model Assistant for Emergency Department Discharge Documentat... JAMA Network Open 2025 33 assisted The LLM only drafts discharge notes that emergency physicians still review and finalize, making it a component in a human-run documentation workflow rather than the agent producing the end result. “the LLM-assisted notes were more complete, correct, concise, and clinically useful than manual notes”
Fluorescence microscopy datasets for training deep neural networks GigaScience 2021 32 not a result The paper's contribution is a dataset resource for others to train deep learning models, not any AI-produced result or solved task. “We provide high quality data sets which can be used to train and evaluate deep learning methods under development.”
Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT... bioRxiv (Cold Spring Harbor Laboratory) 2020 32 assisted The paper's primary contribution is a software toolbox, and the ML models within it served as components of a human-designed meta-analysis workflow whose biomarker findings were interpreted by the researchers, not produced end-to-end by an AI agent. “we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics”
Can CNN‐based species classification generalise across variation in habitat... Methods in Ecology and Evolution 2022 31 assisted The paper is an evaluation of CNN classification performance and generalisability within a human-run ecological monitoring workflow, reporting accuracy metrics on an existing task rather than contributing the classification capability itself as a solved result. “Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively).”
Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery... Scientific Data 2024 30 not a result This is a dataset description paper whose primary contribution is the dataset and annotations themselves, with benchmarks that merely re-run existing state-of-the-art architectures to validate annotation quality. “we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis”
Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic... Diagnostics 2025 30 not a result This is a narrative review synthesizing 67 existing studies on AI in MRI workflows, with no primary result produced by an AI system. “This review addresses that gap by synthesizing evidence on how AI can shorten scanning and reading times, optimize worklist triage, and automate segme...”
It takes guts to learn: machine learning techniques for disease detection fr... Emerging Topics in Life Sciences 2021 30 not a result This is a review/survey of existing machine learning methods for microbiome-based disease classification, offering perspectives rather than producing a new result. “Here, we review current ML methods designed for disease classification from microbiome data.”
Human disease prediction from microbiome data by multiple feature fusion and... iScience 2022 30 assisted This is a methods paper reporting a human-developed framework's competitive benchmark accuracy on existing microbiome disease-prediction tasks, where the deep learning model is an evaluated component demonstrating a method rather than an agent that itself solved the prediction problem. “Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time”
Development and Validation of a Quick Sepsis-Related Organ Failure Assessmen... Journal of Clinical Medicine 2020 29 assisted The paper's contribution is a retrospectively validated clinical decision-support tool that reports AUROC on an existing prediction task (mortality risk scoring, where qSOFA is the established baseline), built and evaluated within a human-run research workflow with output intended to inform clinicians rather than an AI performing a task end to end. “the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three-day mortality was higher than that of the qSOFA scores (0.78 [...”
A hybrid learning strategy for structural damage detection Structural Health Monitoring 2020 29 assisted The paper proposes a hybrid classifier methodology and reports its 'promising performance' on the existing task of damage detection, with humans running the simulations, lab/in-situ tests, and evaluation, making the AI a component in a human-driven validation workflow rather than the agent whose produced result is the paper's contribution. “Results achieved so far attest a promising performance of the hybrid classifier.”
A Smart Contract Vulnerability Detection Method Based on Multimodal Feature... Mathematics 2023 29 assisted The paper proposes a deep-learning detection method and reports its classification accuracies on the existing, well-established task of smart contract vulnerability detection, making the model an evaluated detection tool rather than an AI agent that produced a concrete solved result such as newly discovered vulnerabilities or secured contracts. “Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp...”
Validation of a Deep Learning Chest X-ray Interpretation Model: Integrating... Diagnostics 2023 29 assisted This is a human-run validation study whose contribution is radiologists' graded evaluation of two models' reading quality on an existing task, with the AI explicitly framed as an assistive technology rather than the agent that solved the problem. “two radiologists evaluated the readings provided by KARA-CXR and ChatGPT”
Machine learning in predicting outcomes for stroke patients following rehabi... PLoS ONE 2023 27 not a result This is a systematic review summarizing and critiquing existing ML prediction models rather than producing any new AI-driven result itself. “This systematic review was conducted in accordance with the PRISMA statement and the CHARMS checklist.”
Utilizing physics-based input features within a machine learning model to pr... Wind energy science 2021 27 unsupported no abstract available for this DOI
Machine learning based charge mobility prediction for organic semiconductors The Journal of Chemical Physics 2023 26 assisted The ML models serve as an accelerator for quantum-chemical transfer integral calculations, feeding a human-run charge-transport pipeline whose mobility results are validated against brute-force QC rather than being produced end-to-end by the AI. “We apply these models to studying charge transport in organic crystals with dynamic disorders at 300 K and obtain the charge mobility and anisotropy i...”
Novel machine learning framework for thermal conductivity prediction by crys... SmartMat 2021 25 assisted The abstract's contribution is a human-designed ML framework whose benchmark performance on the existing task of lattice thermal conductivity prediction is reported, so the AI's predictions serve to validate a methodology rather than constituting an AI-produced scientific result. “This machine learning algorithms framework showed superior learning and generalization ability.”
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM... arXiv (Cornell University) 2021 25 unsupported no abstract available for this DOI