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
Reliable and efficient integration of AI into camera traps for smart wildlif... Ecological Informatics 2024 39 unsupported no abstract available for this DOI
Advanced UAV Design Optimization Through Deep Learning-Based Surrogate Model... Aerospace 2024 37 assisted The deep neural network serves as a surrogate model providing performance estimates inside a human-built design/optimization workflow (genetic algorithm, mission scenario selection, case study), rather than the AI itself producing the UAV design result end to end. “This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations.”
Continuous learning of compiler heuristics ACM Transactions on Architecture and Code Optimization 2013 37 unsupported no abstract available for this DOI
Effects of data transformation and model selection on feature importance in... Microbiome 2025 37 assisted The paper is a human-designed methodological assessment where existing ML models serve as evaluation tools across 24 datasets, and the contribution is guidance about transformations rather than any AI-produced result. “Our aim was to assess the impact of various data transformations on the accuracy, generalizability and feature selection by analysis using more than 8...”
MR Images, Brain Lesions, and Deep Learning Applied Sciences 2021 36 not a result This is a literature review/survey of existing deep learning methods for brain lesion segmentation, not a primary result produced by an AI system. “In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of whit...”
Exoplanet detection using machine learning Monthly Notices of the Royal Astronomical Society 2021 36 unsupported no abstract available for this DOI
Machine learning for surrogate process models of bioproduction pathways Bioresource Technology 2022 36 assisted The ML surrogate models merely approximate outputs of human-built process simulations within a human-run technoeconomic/LCA workflow, and the paper is an evaluation of that potential rather than the AI producing a novel result end to end. “The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of mor...”
Why do users override alerts? Utilizing large language model to summarize co... Journal of the American Medical Informatics Association 2024 35 assisted GPT-4 produced summaries of alert comments, but the actual optimization of clinical decision support remains a human expert workflow that the AI output merely informs, and the paper's primary result is an expert-rated quality comparison rather than AI-driven CDS improvement. “These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and of...”
Deep Learning-Based Weed Detection in Turf: A Review Agronomy 2022 35 not a result This is a review article summarizing prior research on DCNN-based weed detection, with no primary result produced by an AI system. “The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision...”
Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systemati... Cancers 2022 33 not a result This is a systematic review synthesizing 28 previously published studies rather than producing a new primary result, and the AI models discussed are the subject of the review, not the paper's own contribution. “This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for auto...”
Machine Learning Techniques for Land Use/Land Cover Classification of Medium... Journal of Environmental Geography 2020 33 assisted The paper's contribution is a human-run comparative evaluation of off-the-shelf ML algorithms (random forest, SVM, ANN) applied to a satellite classification workflow, with the AI models serving as tools inside a human-designed and human-assessed pipeline rather than being the capability that constitutes the contribution. “Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inunda...”
Machine learning phenomics (MLP) combining deep learning with time-lapse-mic... Scientific Reports 2022 33 assisted The deep learning component only extracts latent phenotypes from time-lapse microscopy within a human-run experimental pipeline (cell culture, gene knockdown, drug dosing, proteomics validation) whose biological findings are the actual contribution. “exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for...”
Deep Learning-Based PC Member Crack Detection and Quality Inspection Support... Applied Sciences 2022 32 assisted The AI performs crack detection as one component inside a human-run PC member quality inspection workflow, and the paper's reported contribution is a 'support' system yielding workflow time savings (13 min) rather than the AI solving the inspection task end to end. “Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspe...”
A Review of Deep Learning Advancements in Road Analysis for Autonomous Drivi... Applied Sciences 2024 32 not a result This is a survey/review paper that only summarizes existing deep learning methods for road damage detection and surface classification, producing no primary result of its own. “The main finding of this work is that it offers a comprehensive overview of the current state of the art”
q2-sample-classifier: machine-learning tools for microbiome classification a... bioRxiv (Cold Spring Harbor Laboratory) 2018 32 not a result The paper is a software plugin description plus a benchmark comparison of existing scikit-learn methods on microbiome data, i.e., a toolkit that enables work rather than an AI-produced primary result. “Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning...”
Automatic feature generation for machine learning--based optimising compilat... ACM Transactions on Architecture and Code Optimization 2014 31 unsupported no abstract available for this DOI
Evaluation of Thunderstorm Predictors for Finland Using Reanalyses and Neura... Journal of Applied Meteorology and Climatology 2017 31 unsupported no abstract available for this DOI
Development of postoperative delirium prediction models in patients undergoi... Scientific Reports 2023 30 assisted This is a human-run prospective clinical study in which standard ML algorithms serve as analysis tools to build a risk-stratification model whose predictions are meant to inform clinicians' decisions, with the paper reporting model accuracy (AUROC) on an existing prediction task rather than an AI-produced result solving the problem end to end. “We aimed to develop and validate machine learning models to predict post-cardiovascular surgery delirium.”
BioCoder: a benchmark for bioinformatics code generation with large language... Bioinformatics 2024 30 not a result This is a benchmark/dataset paper whose contribution is an evaluation suite that re-runs existing LLMs on code-generation tests, not a primary scientific result solved by an AI system. “We present BioCoder, a benchmark developed to evaluate LLMs in generating bioinformatics-specific code.”
Prediction of pasture yield using machine learning-based optical sensing: a... Precision Agriculture 2023 29 not a result This is a systematic review/survey of ML-based pasture yield prediction studies, not a primary result produced by an AI system. “Therefore, this review examines the adoption of ML-based optical sensing for predicting the biomass yield of managed grasslands.”
A Review of Large Language Models for Automated Test Case Generation Machine Learning and Knowledge Extraction 2025 29 not a result This is a literature review surveying existing studies on LLM-based test case generation, with no primary result produced by an AI system. “This provides a state-of-the-art review of LLM-based test case generation, emphasizing the potential of LLMs to improve automated testing while identi...”
Single channel blind source separation of complex signals based on spatial‐t... IET Radar Sonar & Navigation 2022 29 unsupported no abstract available for this DOI
Being confident in confidence scores: calibration in deep learning models fo... Remote Sensing in Ecology and Conservation 2024 29 assisted The AI models are the object of study rather than the solver: humans ran the calibration analysis, applied temperature scaling, and drew the methodological conclusions, with the models' predictions serving as components in that human-driven evaluation. “Using a large and diverse European camera trap dataset, we investigate whether deep learning models for species classification in camera trap images a...”
Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Cla... Diagnostics 2021 28 assisted The models are explicitly positioned as diagnostic support tools whose classifications inform pathologists within a human-run clinical workflow, and the paper's contribution is a reported accuracy (AUC 0.94-0.98) on a task pathologists already perform rather than the AI autonomously completing the clinical diagnosis end to end. “it is an area that could benefit from AI-based tools to aid pathologists in their clinical workflows”
Machine learning methods for short‐term bid forecasting in the renewable ene... Wind Energy 2018 27 unsupported no abstract available for this DOI
EM-stellar: benchmarking deep learning for electron microscopy image segment... Bioinformatics 2021 27 unsupported no abstract available for this DOI
Machine learning for the discovery of molecular recognition based on single-... npj Computational Materials 2022 26 assisted The ML model analyzes a human-generated experimental library to understand feature correlations and predict sensor responses, reporting out-of-sample accuracy rather than performing molecular recognition end to end. “Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experiment...”
Enhancement of protein thermostability by three consecutive mutations using... Scientific Reports 2021 26 assisted The machine learning model only ranked candidate mutations in silico to guide which variants humans synthesized and assayed; the thermostability enhancement itself was achieved and validated through the human-run mutagenesis, screening, and wet-lab assay pipeline. “Among 7786 combinations ranked in silico, 20 promising candidates were selected and assayed.”
Intelligent structural design of composite concrete‐encased steel columns ba... Structural Concrete 2025 25 assisted The paper's primary result is a benchmark-style accuracy comparison of a predictive model (BPNN tuned by DMP-PSO) on an existing task, axial capacity prediction from a human-curated database, with the AI serving as a prediction and optimization tool inside a human-run structural design workflow rather than end-to-end producing the constitutive result. “The predictive performance significantly outperforms that of BPNNs optimized using PSO, Bayesian optimization (BO), and grid search methods”
Credit Card Default Prediction: An Empirical Analysis on Predictive Performa... Journal of risk and financial management 2025 25 not a result The paper is a comparative benchmark that re-runs six existing standard methods (LDA, LR, SVM, XGBoost, RF, DNN) on an existing public dataset (UCI Taiwan credit card data), producing only comparative metrics rather than any new solved result or capability. “This article compares the predictive capabilities of six models, namely, linear discriminant analysis (LDA), logistic regression (LR), support vector...”
Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-med... Nature Communications 2021 25 assisted Machine learning (XGBoost) is one analytical component in a human-run CRISPR mutagenesis and deep-sequencing pipeline, modeling editing levels to inform biological understanding rather than performing the task end to end. “We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing.”
Comparing Performance of Machine Learning Algorithms for Default Risk Predic... TEM Journal 2021 24 assisted This is a human-run comparative evaluation of existing ML algorithms on an existing Lending Club dataset, reporting model accuracies rather than presenting an AI-produced solution or new capability, which per the rules makes it assisted. “The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending.”
Ensemble deep learning model for protein secondary structure prediction usin... Results in Engineering 2024 24 unsupported no abstract available for this DOI
Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning Frontiers in Oncology 2020 23 assisted The machine learning (LASSO/Random Forest) is one component in a human-run radiomics workflow, manual tumor segmentation, LIFEx feature extraction, and a final multivariate COX model combining clinical factors, reporting predictive accuracy (AUC 0.79/0.73) on an existing prognostication task rather than the AI itself producing the primary result. “After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor sig...”
Practical fluorescence reconstruction microscopy for large samples and low-m... PLoS Computational Biology 2020 23 assisted The paper's contribution is a human-run contextual evaluation of an existing AI technique (FRM), plus design guidelines and adoption code/manual, with the CNN as a component whose reconstructions the humans assessed rather than a novel result the AI itself produced. “we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in c...”
Cross-modality transformations in biological microscopy enabled by deep lear... Advanced Photonics 2024 23 not a result The abstract explicitly describes a review/structured overview consolidating existing research on cross-modality transformations, with no new primary result produced by an AI system. “we present a structured overview of cross-modality transformations, encompassing applications, data sets, and implementations, aimed at unifying this...”
Learning and interpreting the gene regulatory grammar in a deep learning fra... PLoS Computational Biology 2020 23 assisted The ResNet learned simulated regulatory grammars on synthetic data designed by humans, and the paper's contribution is an interpretability framework and analysis of AI capabilities rather than the AI independently solving a real biological problem. “Our results provide a framework for interpreting the regulatory rules learned by ResNets, and they demonstrate that the ability and efficiency of ResN...”
Transforming Surgical Training With AI Techniques for Training, Assessment,... Journal of Medical Internet Research 2025 22 not a result This is a scoping review that synthesizes 56 existing studies rather than presenting a primary result where an AI system solved a problem itself. “This scoping review examines the integration of AI in surgical training, assessment, and evaluation, aiming to determine how AI technologies can enhan...”
Development and Optimization of VGF-GaAs Crystal Growth Process Using Data M... Crystals 2021 22 assisted The ML models analyzed CFD-generated simulation data to identify influential parameters and provide guidelines, but the actual crystal growth process optimization remains a human-driven downstream task that the AI output merely informs. “capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk cr...”
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Tra... The Planetary Science Journal 2022 22 assisted This is a human-designed exploratory methodology demonstration on a synthetic benchmark dataset, where unsupervised ML techniques (PCA, K-means, manifold learning) serve as components whose outputs inform human interpretation and recommendations rather than the AI itself producing the paper's primary result. “To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra.”