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
BloombergGPT: A Large Language Model for Finance RePEc: Research Papers in Economics 2023 289 unsupported no abstract available for this DOI
DeepPPI: Boosting Prediction of Protein–Protein Interactions with Deep Neura... Journal of Chemical Information and Modeling 2017 285 unsupported no abstract available for this DOI
HyperAttentionDTI: improving drug–protein interaction prediction by sequence... Bioinformatics 2021 274 unsupported no abstract available for this DOI
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Chal... IEEE Transactions on Medical Imaging 2019 273 not a result The paper's contribution is the organization of a benchmark/challenge that evaluates eight teams' methods, not an AI system itself producing a solved result. “To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating.”
Deep learning and the electrocardiogram: review of the current state-of-the-... EP Europace 2020 263 not a result This is a review/survey paper orienting clinicians to deep learning for ECG analysis, with no primary result produced by an AI system. “This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and cu...”
CRISPR–Cas9 gRNA efficiency prediction: an overview of predictive tools and... Nucleic Acids Research 2022 241 not a result This is a review/survey of existing gRNA prediction tools with a benchmark re-evaluation of others' methods, not a primary result produced by an AI system. “Here, we discuss the approaches that pertain to the on-target activity problem... we evaluate these tools on independent datasets and give some sugges...”
The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment... Bulletin of the American Meteorological Society 2024 211 assisted The paper's contribution is a human-run statistical verification of an existing ML model's (PanguWeather) forecast quality against an NWP system, an assessment of AI output rather than a result produced by the AI, with the AI serving only as the forecast-generating component in a human-completed evaluation. “we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (Pan...”
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and dee... Nature Communications 2021 206 assisted CRISPRon is a prediction tool whose efficiency scores inform human gRNA design decisions (delivered as a design webserver), and the abstract reports benchmark accuracy of a model on an existing prediction task, making the AI a component of the human-run design workflow rather than the agent that produced the paper's primary result. “The design of CRISPR gRNAs requires accurate on-target efficiency predictions”
Deep learning for post-processing ensemble weather forecasts Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences 2021 196 assisted The deep neural network is a post-processing step layered on top of human-run numerical ensemble simulations, improving their output rather than producing the weather forecast end to end. “We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks.”
Predicting gastric cancer outcome from resected lymph node histopathology im... Nature Communications 2021 186 assisted The deep-learning model performs tumor/lymph-node detection and computes the T/MLN ratio as a measurement component inside a human-designed study, with the prognostic-factor demonstration and cohort validation carried out by researchers, and the authors themselves frame the system as assisting pathologists and oncologists rather than producing the prognostic result end-to-end. “These findings indicate that deep-learning models could assist not only pathologists in detecting lymph nodes with metastases but also oncologists in...”
Machine learning in chemical reaction space Nature Communications 2020 184 assisted The ML provides learned reaction energies that inform human-driven extraction of reduced reaction networks and microkinetic analysis, within a workflow whose primary contribution is a newly established database and modeling framework rather than an AI-produced end result. “the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses”
A Deep Learning Approach Based on Explainable Artificial Intelligence for Sk... IEEE Access 2022 178 assisted The model is a decision-support tool whose benchmark performance (accuracy metrics on the existing ISIC 2019 task) is intended to inform dermatologists, who remain the agents completing the actual diagnosis. “This will help the dermatologists to make rational diagnosis in the early stages of skin cancer.”
Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learn... Entropy 2020 172 assisted The CNN serves only as a pre-trained feature extractor inside a human-designed CAD pipeline (preprocessing, handcrafted ABCD features, MI-based fusion, and separate classifiers), so the AI is one component of a workflow humans built and ran rather than the agent producing the classification result end to end. “deep learning features are extracted using a Convolutional Neural Network (CNN) architecture, which is pre-trained on Imagenet”
Search Personalization Using Machine Learning Management Science 2020 167 unsupported no abstract available for this DOI
Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis:... Infectious Diseases and Therapy 2022 159 assisted The paper reports a predictive model's accuracy (XGBoost, AUC 0.884) on an existing task (sepsis mortality prediction in MIMIC-IV) whose output is meant to inform clinicians rather than the AI itself producing the clinical result end-to-end. “the eXtreme Gradient Boosting (XGBoost) model achieved the best performance with an AUC of 0.884 and an accuracy of 89.5% in the validation cohort”
Deep Learning Models for Retinal Blood Vessels Segmentation: A Review IEEE Access 2019 155 not a result This is a survey/review paper characterizing existing deep learning segmentation methods and offering recommendations, with no primary result produced by an AI system. “This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis.”
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and... Sensors 2022 154 not a result The abstract explicitly identifies this as a review paper providing an overview of existing radar deep learning work, with no primary result produced by an AI system. “This review paper attempts to provide a big picture of the deep radar perception stack”
An effective self-supervised framework for learning expressive molecular glo... Briefings in Bioinformatics 2021 151 assisted The paper contributes a pre-training framework/encoder whose value is demonstrated as accuracy on 14 existing benchmark prediction tasks, positioning the AI as a molecular-encoder component for human-run drug discovery pipelines rather than as the agent that solved a discovery problem. “The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.”
Deep Learning in Image-Based Plant Phenotyping Annual Review of Plant Biology 2024 137 not a result This is a review/survey of deep learning applications in plant phenotyping, presenting no primary result produced by an AI system. “Here, we review the basics of deep learning, assessment of deep learning success, examples of applications of deep learning in plant phenomics, best p...”
A Machine Learning Approach for the Differential Diagnosis of Alzheimer and... Frontiers in Neuroinformatics 2020 133 assisted The paper reports the classification accuracies of ML algorithms (ANN, SVM, ANFIS) on an existing diagnostic task, framing the system as a decision-support tool for physicians rather than an agent that performs the differential diagnosis end to end. “therefore suggesting its usefulness in supporting physicians' diagnostic evaluations”
Advances in Remote Sensing Technology, Machine Learning and Deep Learning fo... Remote Sensing 2020 129 not a result This is a review/survey paper synthesizing past and current approaches to oil spill detection and prediction, not presenting a primary AI-produced result. “we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill di...”
Expansion of RiPP biosynthetic space through integration of pan-genomics and... PLoS Biology 2020 127 assisted The SVM-based decRiPPter only prioritized candidate RiPP gene clusters, while the actual discovery, elucidation of the novel class V lanthipeptide pristinin A3, was completed by humans through NMR, MS/MS, and chemical labeling. “The 2D structure of the new RiPP, which we name pristinin A3 (1), was solved using nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS)...”
Systematic Review of Deep Learning and Machine Learning for Building Energy Frontiers in Energy Research 2022 126 not a result This is a systematic review/survey that evaluates and taxonomizes existing ML/DL techniques from the literature rather than producing a new result itself. “The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the perform...”
Application of machine learning for advanced material prediction and design EcoMat 2022 123 unsupported no abstract available for this DOI
Land Use and Land Cover Classification Meets Deep Learning: A Review Sensors 2023 123 not a result This is a systematic survey/review of deep-learning-based LULC classification methods, which presents no primary result of its own. “In this paper, we present a systematic review of deep-learning-based LULC classification”
Deep learning for accelerated and robust MRI reconstruction Magnetic Resonance Materials in Physics Biology and Medicine 2024 119 not a result The abstract explicitly states this is a review paper surveying existing DL approaches for MRI reconstruction, with no primary result produced. “This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction”
A method for classifying medical images using transfer learning: A pilot stu... IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) 2017 115 unsupported no abstract available for this DOI
Quantitative interpretation explains machine learning models for chemical re... Nature Communications 2021 114 not a result The paper's contributions are an interpretability framework, bias diagnostics of an existing model, and a new benchmark dataset, tools and a benchmark that enable future work rather than an AI-produced primary result, with the Molecular Transformer being the object of study rather than the agent that solved anything. “We present a new debiased dataset that provides a more realistic assessment of model performance, which we propose as the new standard benchmark for c...”
Damage detection in a novel deep-learning framework: a robust method for fea... Structural Health Monitoring 2019 110 assisted The paper's contribution is a new CNN architecture whose performance is reported as accuracy gains on existing damage-detection benchmark datasets (numerical simulations and lab measurements), i.e., an improved predictive tool within the human-run structural health monitoring workflow rather than the AI itself producing a novel solved result. “The results show that our proposed method has accuracy improvements of at least 10% over other network architectures.”
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and... IEEE International Conference on Electronics and Nanotechnology (ELNANO) 2018 109 assisted The paper reports a CNN's validation loss/accuracy on an existing TB-detection task, with its actual contribution being the methodological comparison of segmentation and augmentation strategies rather than the AI end-to-end solving the diagnostic problem. “They demonstrate the efficiency of lung segmentation, lossless and lossy data augmentation for CADx of tuberculosis by deep convolutional neural netwo...”
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic... JMIR Medical Informatics 2022 106 not a result This is a systematic review/survey of existing deep learning methods on ECG data, which presents no primary result of its own. “This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.”
Fitting potential energy surfaces with fundamental invariant neural network.... The Journal of Chemical Physics 2020 103 assisted The paper's contribution is a human-developed computational scheme for generating fundamental invariants (based on invariant theory and parallel programming) that serves as input to neural networks, making the NN merely one fitting component inside a human-designed PES construction workflow rather than the agent that produced the result. “we report an improved and efficient generation scheme of FIs based on the computational invariant theory and parallel program, which can be readily us...”
Drug-drug interactions prediction based on deep learning and knowledge graph... iScience 2024 102 not a result This is a survey/review that summarizes and compares existing DDI prediction methods rather than producing a new result itself. “In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge.”
Generative deep learning enables the discovery of a potent and selective RIP... Nature Communications 2022 101 assisted The GDL model generated a virtual candidate library, but the actual discovery of the validated RIPK1 inhibitor came from a human-run pipeline of virtual screening, synthesis, and in vitro/in vivo bioactivity evaluation, making the AI one component in a workflow humans completed. “Virtual screening against the generated tailor-made compound library and subsequent bioactivity evaluation lead to the discovery of a potent and selec...”
A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology... Cancers 2022 91 assisted The deep learning model reports classification performance (ROC AUCs) on an existing screening task, but the abstract explicitly frames it as a pilot study with potential for 'aiding' human-run screening processes rather than the AI itself performing cervical cancer screening end to end. “demonstrating the promising potential use of such models for aiding the screening processes for cervical cancer”
Single-cell multiome of the human retina and deep learning nominate causal v... Cell Genomics 2022 88 assisted Deep learning models are one predictive component within a human-driven multi-omics pipeline (single-cell atlas, HiChIP, eQTL integration), and the outcome is nomination of candidate variant-gene pairs rather than the AI itself producing a solved result. “Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders”
Machine Learning as a Tool for Hypothesis Generation The Quarterly Journal of Economics 2024 85 unsupported no abstract available for this DOI
Evaluation of an artificial intelligence clinical trial matching system in A... JAMIA Open 2020 85 assisted The paper evaluates an AI trial-matching tool whose eligibility predictions are prescreening outputs within a clinician-led workflow, with the gold standard and final decisions made by clinicians, making it a decision-support component rather than an end-to-end solution. “however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen...”
LM-GVP: an extensible sequence and structure informed deep learning framewor... Scientific Reports 2022 85 assisted This is a methods paper whose reported results are benchmark accuracy on existing property-prediction tasks, with the framework explicitly positioned as a tool to accelerate future protein engineering and drug development rather than an AI-produced solution to a scientific problem. “Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and p...”
Automated Explainable Multidimensional Deep Learning Platform of Retinal Ima... JAMA Network Open 2021 83 assisted This is a diagnostic accuracy study benchmarking a deep learning platform's classification performance against expert ophthalmologists on an existing clinical task, with the platform positioned as a decision-support aid for routine human-run ROP screening rather than an autonomous solver. “Results suggest that a deep learning platform could identify and classify multidimensional ROP pathological lesions in retinal images with high accura...”