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
Data-driven Chemical Reaction Prediction and Retrosynthesis CHIMIA International Journal for Chemistry 2019 32 not a result This is a review article surveying existing reaction prediction and retrosynthesis approaches, not a paper reporting a primary result produced by an AI system. “In this article, we briefly review different approaches to predict forward reactions and retrosynthesis, with a strong focus on data-driven ones.”
The Application of Machine Learning on Antibody Discovery and Optimization Molecules 2024 32 not a result The abstract explicitly identifies the paper as a review surveying machine learning-based antibody discovery developments rather than presenting a primary result produced by an AI system. “This review focuses on the latest machine learning-based antibody discovery and optimization developments.”
MAGPIE: accurate pathogenic prediction for multiple variant types using mach... Genome Medicine 2024 31 assisted The abstract reports a machine-learning model's benchmark accuracy on an existing task (variant pathogenicity prediction against ClinVar-derived test and validation sets) and positions MAGPIE as a tool whose scores inform human variant interpretation, rather than an AI agent that end-to-end produced a solved primary result. “results underline the robustness of MAGPIE as a valuable tool for predicting pathogenicity in various types of human genome variations”
Detecting an axion-like particle with machine learning at the LHC Journal of High Energy Physics 2021 31 assisted The CNN is a tagging component inside a human-designed physics analysis whose contribution is projected sensitivity bounds on ALP couplings, not an AI-produced detection result. “With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3 GeV to 5 GeV...”
Machine Learning Approaches for Stroke Risk Prediction: Findings from the Su... Journal of Cardiovascular Development and Disease 2024 30 assisted Human researchers designed and ran this cohort analysis, employing standard ML models as analytical tools and reporting their performance metrics on the existing task of stroke risk prediction, with the model outputs (clusters, SHAP rankings) informing the authors' epidemiological conclusions rather than constituting an end-to-end AI-produced solution. “machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessme...”
Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivi... Viruses 2023 30 assisted The ML models only produced in-silico predictions of candidate inhibitors (unvalidated 'potential' repurposed drugs) within a human-built QSAR pipeline, rather than the AI itself solving the antiviral discovery problem end to end. “We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values.”
Deep learning-based prediction of in-hospital mortality for sepsis Scientific Reports 2024 30 assisted The DGFSD model is a clinical decision-support tool that reports superior accuracy on the existing task of sepsis mortality risk scoring, with its output explicitly framed as informing physicians who make the actual clinical decisions, so the AI assists rather than solves. “can help physicians in Intensive Care Unit make optimal clinical decisions”
Machine learning applications on neonatal sepsis treatment: a scoping review BMC Infectious Diseases 2023 29 not a result This is a scoping review surveying existing studies of machine learning for neonatal sepsis, so it contains no primary result that an AI system solved. “This review aimed to present the application of machine learning on neonatal sepsis treatment.”
Revolutionizing sepsis diagnosis using machine learning and deep learning mo... BMC Infectious Diseases 2025 29 not a result This is a systematic literature review synthesizing existing ML/DL sepsis prediction studies, not a primary result produced by an AI system. “This systematic review analyzes and synthesizes existing ML/DL approaches applied to sepsis prediction”
Multi‐Agent‐Network‐Based Idea Generator for Zinc‐Ion Battery Electrolyte Di... Advanced Materials 2025 29 assisted The LLM network's role was limited to proposing/identifying candidate electrolyte compositions, while all actual discovery results (synthesis, conductivity, cycling performance, spectroscopic characterization) were produced by human-run wet-lab experiments, making this idea generation inside a human pipeline rather than the AI itself producing the result. “this work presents a Large Language Model (LLM)‐based multi‐agent network that proposes DEE compositions for zinc‐ion batteries”
Deep learning approaches for non-coding genetic variant effect prediction: c... Briefings in Bioinformatics 2024 29 not a result This is a survey/review paper summarizing existing deep learning approaches for non-coding variant effect prediction, with no primary result produced by an AI system. “Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their...”
Enhancing PET Degrading Enzymes: A Combinatory Approach ChemBioChem 2024 29 assisted Machine learning is listed as one of several combined strategies (alongside rational design and ancestral sequence reconstruction) in a human-run enzyme engineering workflow that produced and validated the Combi-PETase variant, so the AI informed the design rather than producing the result itself. “we combine different strategies such as structure‐based rational design, ancestral sequence reconstruction and machine learning to engineer a more hig...”
Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explain... Tropical Medicine and Infectious Disease 2024 27 assisted The AI components (RF, LIME, GPT) produce predictions and explanations that inform healthcare workers who remain the diagnostic decision-makers, and the paper's contribution is an interpretability framework rather than an AI end-to-end solving of diagnosis. “This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like G...”
Review of Methods for Automatic Plastic Detection in Water Areas Using Satel... Sensors 2024 27 not a result This is a survey/review paper summarizing existing projects, data acquisition techniques, and deep learning algorithms for plastic detection, with no primary result of its own. “This review presents the main projects and research aimed at solving the “plastic” problem.”
Deep learning for electrocardiogram interpretation: Bench to bedside European Journal of Clinical Investigation 2025 26 not a result The abstract is a generic introductory discussion of deep learning's potential for ECG analysis with no primary result, model, or finding reported, consistent with a review/commentary. “Recent advancements in deep learning (DL), a subset of artificial intelligence, have shown the potential to automate and improve disease recognition,...”
Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competitio... bioRxiv (Cold Spring Harbor Laboratory) 2025 26 not a result The paper is a summary and analysis of a crowdsourced competition, reporting participants' designs, lessons learned, and recommendations for future competitions, rather than presenting a primary result produced by an AI system. “In this report, we summarize and analyze the 2024 Adaptyv protein design competition.”
Machine learning accelerated descriptor design for catalyst discovery in CO2... npj Computational Materials 2025 25 assisted The machine-learned force fields and unsupervised ML serve as a computational screening tool within a human-designed discovery framework, producing untested candidate predictions rather than a validated catalyst discovery. “we offer a powerful tool for catalyst discovery. We propose new promising candidates such as ZnRh and ZnPt3, which to our knowledge, have not yet been...”
Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection an... Electronics 2024 22 not a result This is a systematic literature review surveying existing research on AI-based malaria detection, not a primary result produced by an AI system. “Here, we present a systematic literature review of recent research on the use of automated algorithms to identify and classify malaria parasites in bl...”
Data-driven enzyme engineering to identify function-enhancing enzymes Protein Engineering Design and Selection 2022 22 not a result The abstract explicitly states this is a review of recent progress in data-driven enzyme engineering models, with no primary result produced. “Here, we reviewed recent progresses of data-driven models that can be applied in identifying efficiency-enhancing mutants for catalytic reactions.”
Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 sat... Environmental Monitoring and Assessment 2025 20 assisted This is a human-run remote-sensing study in which off-the-shelf RF and gradient boosting served as the regression component: the abstract reports model accuracy metrics on the existing task of AGB estimation rather than contributing an AI capability that performs the task end to end. “RF achieved the highest R2 value of 0.5755 Mg/ha, while gradient boosting achieved the highest R2 value of 0.7298 Mg/ha”
Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospec... Advanced Energy Materials 2025 20 not a result This is a review/survey of interpretable machine learning techniques for battery prognosis, with no primary result produced by an AI system. “this review systematically examines state‐of‐the‐art interpretable battery lifetime prediction techniques”
Predicting dynamic stability from static features in power grid models using... Chaos An Interdisciplinary Journal of Nonlinear Science 2024 15 unsupported no abstract available for this DOI
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture C... Remote Sensing 2023 14 unsupported no abstract available for this DOI
Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box A... Sensors 2021 1 assisted The paper's contribution is a methodological resampling/white-box framework for understanding CNN uncertainties, with the CNN's detection performance reported as accuracy metrics on the existing GW detection task rather than the AI itself producing the scientific result. “we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis”
Faculty Opinions recommendation of A clinically applicable approach to conti... Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature 2019 not a result the record is "faculty opinions recommendation", so it is a note about another work rather than a primary result: there is nothing here for an AI to have solved “Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administrat...”
Basset: learning the regulatory code of the accessible genome with deep conv... Genome Research 2016 1,047 assisted Basset is a predictive tool whose demonstrated contribution is accuracy on an existing prediction task and variant-annotation output that informs researchers' interpretation of the noncoding genome, rather than an AI-produced solution to a primary scientific problem. “Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.”
DeepCRISPR: optimized CRISPR guide RNA design by deep learning Genome biology 2018 634 assisted DeepCRISPR is a predictive design tool whose sgRNA efficacy/off-target predictions inform human-run CRISPR design workflows rather than the AI itself performing or producing the end biological result, and the abstract's claim is benchmark superiority on an existing in silico prediction task. “surpassing available state-of-the-art in silico tools”
Machine learning for perovskite materials design and discovery npj Computational Materials 2021 536 not a result This is a review paper surveying existing ML applications in perovskite discovery, with no primary result produced by an AI system. “In this review, we retrospected the latest applications of ML in assisting perovskites discovery.”
Democratising deep learning for microscopy with ZeroCostDL4Mic Nature Communications 2021 524 not a result The paper presents a toolkit/platform that wraps pre-existing DL networks (U-Net, StarDist, YOLOv2, CARE, etc.) in easy-to-use Colab notebooks to enable others' work, rather than producing a new AI-driven result itself. “Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google C...”
A comparison of machine learning algorithms for diabetes prediction ICT Express 2021 490 not a result The paper merely re-runs seven standard ML algorithms on the public UCI Pima Indian Diabetes benchmark dataset and reports comparative accuracies, producing no new solved problem or capability. “We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository... We used seven ML algorithms on...”
CADD v1.7: using protein language models, regulatory CNNs and other nucleoti... Nucleic Acids Research 2023 440 assisted CADD is a variant-scoring tool whose predictions serve as one input to aid human clinical assessment and genetic studies, and the paper's contribution is improved benchmark performance of that predictive component rather than an AI end-to-end solving of a problem. “Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants...”
Brain age prediction using deep learning uncovers associated sequence varian... Nature Communications 2019 393 assisted The deep learning model produced brain age predictions that served as an intermediate phenotype for a human-run GWAS pipeline, meaning the AI's output was one component in a larger workflow rather than the end-to-end solution of the primary biological finding. “A genome-wide association study (GWAS) of PAD in the UK Biobank data... yielded two sequence variants”
Early detection of type 2 diabetes mellitus using machine learning-based pre... Scientific Reports 2020 366 assisted The paper is a comparative evaluation of ML models versus regression for T2DM screening within a human clinical workflow, not a demonstration that an AI system itself solved early diabetes detection; the models are prediction components whose outputs inform clinicians, and the contribution is the benchmark comparison concluding no clinically relevant improvement. “This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of...”
Predicting protein–protein interactions through sequence-based deep learning Bioinformatics 2018 366 assisted The paper's contribution is a new prediction method whose primary result is benchmark accuracy on an existing task, with its predictions framed as computational tools to guide human discovery of PPIs rather than an end-to-end result produced autonomously by the AI. “Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (...”
Current applications and challenges in large language models for patient car... Communications Medicine 2025 342 not a result This is a systematic review synthesizing existing literature on LLM applications in patient care; it produces a taxonomy and framework rather than any primary result, and no AI system performed the task itself. “This review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementa...”
Crowdsourcing the creation of image segmentation algorithms for connectomics Frontiers in Neuroanatomy 2015 341 not a result The paper's contribution is the organization, scoring analysis, and retrospective evaluation of a segmentation challenge (a benchmark/competition report with recommendations for future challenges), not an AI system's own solution to a scientific problem. “we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain”
Interpretable scientific discovery with symbolic regression: a review Artificial Intelligence Review 2024 330 not a result This is a survey/review of symbolic regression methods with no primary scientific result produced by an AI system. “In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model disc...”
Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by dee... Nature Communications 2019 330 assisted The deep learning model is a gRNA activity predictor built from human-run genome-scale screens; it informs guide design rather than performing genome editing end-to-end, and the paper's contribution is a benchmarked prediction tool within a human workflow. “Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate...”
A unified connectomic target for deep brain stimulation in obsessive-compuls... Nature Communications 2020 316 assisted The connectivity-derived models served as an analytical component within a human-run workflow (surgery, cohort data collection, validation), informing researchers and surgeons rather than autonomously producing the clinical outcome, which came from human-performed DBS. “connectivity-derived models may inform clinical improvements across DBS targets, surgeons and centers”
Creating an automated trigger for sepsis clinical decision support at emerge... PLoS ONE 2017 308 assisted The ML model is a retrospective prediction component (a trigger) for sepsis clinical decision support, reporting AUC improvements to inform clinicians rather than performing the clinical task end to end. “utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection”