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
Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D im... Light Science & Applications 2025 22 assisted The AI (UI-Trans) is a restoration component inside a human-run light sheet microscopy pipeline, the microscope hardware acquires the low-exposure 4D data and the network denoises it, so the imaging result comes from the combined system rather than the AI alone. “we present a deep-learning enhanced light sheet fluorescence microscopy (LSFM) approach that addresses the restoration of rapid volumetric time-lapse...”
Limits of Prediction for Machine Learning in Drug Discovery Frontiers in Pharmacology 2022 21 not a result This is a benchmark-style evaluation that re-runs six standard ML algorithms on 243 existing datasets to characterize extrapolation error, with no primary discovery result produced or solved by the AI. “this study examined the capabilities of six machine learning algorithms to extrapolate from 243 datasets”
A Customized Multi‐Scale Deep Learning Framework for Storm Nowcasting Geophysical Research Letters 2023 21 assisted The paper's contribution is a plug-in methodological framework whose value is demonstrated through improved skill scores on the existing SEVIR nowcasting benchmark, a methods-improvement/benchmark-accuracy paper rather than the AI system itself delivering nowcasting as the paper's primary result. “The quantitative evaluation shows that all models applying our framework can improve skill scores by 8.5%–42.6% for 1‐hr nowcasting.”
Learning to quantify uncertainty in off-target activity for CRISPR guide RNA... Nucleic Acids Research 2024 21 assisted crispAI is an in-silico predictor whose uncertainty estimates and efficiency scores serve as decision support for human sgRNA designers, with the paper's results being benchmark accuracy/calibration comparisons against existing methods rather than the AI itself producing a completed outcome. “providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design”
Machine learning enabled discovery of application dependent design principle... Machine Learning Science and Technology 2020 18 assisted The neural networks served as a property-prediction surrogate enabling a human-run screening pipeline, and the paper's actual contribution, the design principles, was derived by the authors through statistical analysis of the AI-screened candidates rather than produced by the AI itself. “By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the s...”
Deep Learning for Line Intensity Mapping Observations: Information Extractio... The Astrophysical Journal Letters 2021 18 assisted The cGAN demonstrates a denoising/reconstruction capability entirely on simulated mock observation maps rather than solving the actual LIM observational problem, making it a method-development study whose AI output would inform future real-data analysis. “We train the cGANs using 30,000 mock observation maps with assuming a Gaussian noise matched to the expected noise level of NASA's SPHEREx mission.”
Do Machine Learning Approaches Offer Skill Improvement for Short-Term Foreca... Weather and Forecasting 2022 16 assisted This is a human-designed systematic skill assessment of ANN models on ERA5 reanalysis data, where the AI is the object of evaluation rather than the agent producing the paper's result, and the models explicitly fail to fully solve the task (50% underestimation of strong gusts). “This study systematically assesses the skill enhancement that can be achieved using artificial neural networks (ANNs) for forecasting of wind gust occ...”
Integrating Satellite Imagery and Ground-Based Measurements with a Machine L... Hydrology 2023 15 assisted The random forest classifier is one component in a human-designed remote sensing workflow, humans selected the sites, assembled the satellite/in-situ data, and performed the trend and hydro-climatological factor analysis that constitutes the paper's actual findings, so the AI-assisted delineation only informed a human-completed monitoring study. “The random forest (RF) supervised classification algorithm was applied to estimate the lake area extent using Landsat imagery that was acquired betwee...”
Temporal and spatial satellite data augmentation for deep learning-based rai... Journal of Hydroinformatics 2024 15 assisted The paper's contribution is a human-designed comparative assessment of data augmentation techniques on an existing ConvLSTM nowcasting pipeline, with the AI model serving as the prediction instrument rather than being the demonstrated capability itself. “The goal of the study is to assess the impact of data augmentation on flood nowcasting.”
Leveraging Large Language Models for Enhancing Literature-Based Discovery Big Data and Cognitive Computing 2024 15 assisted The paper's contribution is a human-designed LBD framework/methodology where LLMs serve as enhancement components producing untested candidate hypotheses (on known illustrative scenarios like garlic and blood pressure) that would inform downstream human research, not an AI-produced discovery result. “Empirical validations, including scenarios on the effects of garlic on blood pressure and nutritional supplements on health outcomes, demonstrate the...”
GSpyNetTree: a signal-vs-glitch classifier for gravitational-wave event cand... Classical and Quantum Gravity 2024 14 assisted The paper presents a CNN classifier tool designed as one automation component within the human-run LIGO-Virgo candidate validation pipeline, rather than an AI-produced result where the classification itself is the solved problem. “LIGO-Virgo GW event candidate validation will require increased levels of automation”
A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/... Crystals 2025 12 not a result This is a survey/review paper summarizing ML methods for crystal structure and property prediction, with no primary result of its own. “This review provides a comprehensive overview of the evolution of CSP and CPP methodologies”
SciDaSynth: Interactive Structured Data Extraction From Scientific Literatur... Campbell Systematic Reviews 2025 12 assisted SciDaSynth is an interactive human, AI collaborative tool in which the LLM generates candidate tables but researchers query, validate, and refine the outputs, so the structured data was produced through a human-run workflow rather than by the AI end-to-end. “A within‐subjects study with nutrition and NLP researchers demonstrates SciDaSynth's effectiveness in producing high‐quality structured data more effi...”
An Automatic Source Code Vulnerability Detection Approach Based on KELM Security and Communication Networks 2021 12 assisted The abstract reports a model's comparative performance metrics (training speed, precision vs. Bi-LSTM/word2vec) on the existing task of vulnerability detection, making the AI a detection component in a workflow rather than an agent that itself produced a novel detection result. “Experimental results show that doc2vec vector representation brings faster training and better generalizing performance than word2vec. ELM converges m...”
Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With Hi... Journal of Polymer Science 2024 10 assisted The AI (VAE plus SVM) served as a predictive/screening component in a human-run design workflow, humans performed systematic molar ratio adjustments and used the model's fingerprints and Tg predictions to select candidates, rather than the AI itself end-to-end producing a validated discovery. “Through systematic molar ratio adjustments and VAE‐based fingerprinting, we discover novel SMP candidates with Tg values between 190°C and 200°C”
Challenges in Testing Large Language Model-Based Software: A Faceted Taxonom... ACM Transactions on Software Engineering and Methodology 2026 4 not a result The paper's contribution is a taxonomy of test-case design challenges and open problems, not a primary result that any AI system produced; the LLM-assisted framework analysis is merely illustrative support for the taxonomy. “This article presents a taxonomy for LLM test case design, informed by research literature and our experience.”
Discovery of EP4 antagonists with image-guided explainable deep learning wor... National Science Open 2025 1 assisted The deep learning workflow prioritized candidate EP4 antagonists, but the actual discovery, synthesis and wet-lab confirmation of nanomolar activity, selectivity, and macrophage assays, was completed by humans, making the AI a component in a human-run drug discovery pipeline. “Using LeadDisFlow, we identified four potent antagonists with single-nanomolar antagonistic activity against PGE2 receptor subtype 4 (EP4)”
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Des... venue not stated 2018 unsupported no DOI on the record
Locality Sensitive Deep Learning for Detection and Classification of Nuclei... IEEE Transactions on Medical Imaging 2016 1,217 unsupported no abstract available for this DOI
Medical image segmentation using deep learning: A survey IET Image Processing 2022 845 not a result This is a survey paper that reviews and classifies existing literature on deep learning for medical image segmentation, producing no primary result of its own. “we present a comprehensive thematic survey on medical image segmentation using deep learning techniques”
Machine learning paradigm for structural health monitoring Structural Health Monitoring 2020 372 not a result The abstract describes a perspective/review article that proposes a conceptual paradigm and surveys challenges rather than presenting an AI system that produced a primary result. “This article sheds light on principles for machine learning paradigm for structural health monitoring with some examples and reviews the existing chal...”
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Dr... Journal of Chemical Information and Modeling 2018 345 not a result The paper's contribution is a proposed evaluation metric for generative models, not a scientific result produced by an AI system; it is a tool/framework enabling future work. “We propose an evaluation metric for generative models called Fréchet ChemNet distance (FCD).”
Explainable machine learning can outperform Cox regression predictions and p... Scientific Reports 2021 335 assisted This is a human-run comparative evaluation that reports c-index accuracy of off-the-shelf ML models against Cox regression on an existing prediction task; the AI's outputs informed the authors' methodological conclusions rather than the AI itself producing the paper's result as a new end-to-end capability. “We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression”
Multimodal skin lesion classification using deep learning Experimental Dermatology 2018 323 assisted The paper's contribution is a method (multimodal fusion) that improves a classifier's benchmark accuracy on an existing diagnostic task, positioning the AI as a clinical decision-support component whose performance is evaluated against human-provided labels rather than an AI system itself delivering the solved result. “We showed that our multimodal classifier outperforms a baseline classifier that only uses a single macroscopic image in both binary melanoma detection...”
Design of target specific peptide inhibitors using generative deep learning... Nature Communications 2024 319 assisted The VAE generated candidate sequences, but the final inhibitors were produced through a human-run pipeline of Rosetta docking, MD filtering, and wet-lab experimental assays, making the AI one generative component rather than the agent that solved the design task. “Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affini...”
Learned protein embeddings for machine learning Bioinformatics 2018 313 unsupported no abstract available for this DOI
DeepChrome: deep-learning for predicting gene expression from histone modifi... Bioinformatics 2016 305 unsupported no abstract available for this DOI
Deep neural network correlation learning mechanism for CT brain tumor detect... Neural Computing and Applications 2021 284 assisted The paper's contribution is a proposed learning mechanism (CLM) whose value is demonstrated through standard accuracy/precision/recall metrics on an existing CT brain tumor detection task, i.e., a model-performance report inside the broader human-run medical imaging workflow rather than the AI itself producing the solved result. “Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall.”
Combining mechanistic and machine learning models for predictive engineering... Nature Communications 2020 282 assisted The machine learning models provided design recommendations within a human-run metabolic engineering pipeline (genome-scale model target selection, library construction, biosensor screening), with humans building and validating the improved strains rather than the AI producing the result end to end. “the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the...”
A machine learning Automated Recommendation Tool for synthetic biology Nature Communications 2020 277 assisted ART is a recommendation/prediction tool whose outputs guide human engineers who still design, build, and test the strains in wet-lab DBTL cycles, making the AI a component in a human-run pipeline rather than the agent that solved the bioengineering. “ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels.”
Machine Learning and Deep Learning Methods for Skin Lesion Classification an... Diagnostics 2021 270 not a result This is a systematic review synthesizing prior published studies rather than presenting a new AI-produced result. “This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems.”
Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT... Genome biology 2021 265 assisted The paper's primary contribution is a software toolbox, and the ML models within it served as components of a human-designed and human-run meta-analysis pipeline (data curation, augmentation strategy, biomarker interpretation) rather than autonomously producing the result. “we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagen...”
Process monitoring and machine learning for defect detection in laser-based... Journal of Intelligent Manufacturing 2023 239 not a result This is a critical review/survey of ML approaches for defect detection in additive manufacturing, not a primary result produced by an AI system. “the application of various Machine Learning (ML) approaches and in-situ monitoring technologies for the purpose of defect detection are reviewed”
Air Quality Index and Air Pollutant Concentration Prediction Based on Machin... Applied Sciences 2019 226 assisted This is a human-run application/evaluation study in which researchers applied off-the-shelf algorithms (SVR, RFR) to two existing public datasets and reported accuracy metrics, so the AI served as a predictive component in a workflow the humans built, ran, and interpreted rather than being the agent that produced the paper's primary result. “We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Be...”
A deep-learning framework for multi-level peptide–protein interaction predic... Nature Communications 2021 222 assisted The abstract presents CAMP as a benchmark-evaluated prediction tool whose outputs are meant to inform human researchers conducting peptide drug discovery, rather than the AI itself producing a solved scientific result. “CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can...”
Automated language essay scoring systems: a literature review PeerJ Computer Science 2019 179 unsupported no abstract available for this DOI
A machine-learning approach for structural damage detection using least squa... Structural Health Monitoring 2016 169 unsupported no abstract available for this DOI
Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine L... Advanced Energy Materials 2020 165 assisted The primary results (HER screening, adsorption energetics, reaction pathways) come from human-run DFT calculations, with the ML model serving as a corroborating prediction component whose output 'indicates a similar result' and only informs guidelines for experimental design. “the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of...”
Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper SIAM Journal on Imaging Sciences 2023 151 unsupported no abstract available for this DOI
The emergence of large language models as tools in literature reviews: a lar... Journal of the American Medical Informatics Association 2025 145 not a result This is a systematic review/survey summarizing existing literature on LLM usage in review automation, which falls under reviews that only synthesize others' work, regardless of the AI assistance embedded in its own pipeline. “Our LLM-assisted systematic review revealed a significant number of research projects related to review automation using LLMs.”