Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 64,181 to 64,190 of 231,309 articles

Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications

arXiv Jan 15, 2026
The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effec... read more 
Machine Learning

Difficulty-guided Sampling: Bridging the Target Gap between Dataset Distillation and Downstream Tasks

arXiv Jan 15, 2026
In this paper, we propose difficulty-guided sampling (DGS) to bridge the target gap between the distillation objective and the downstream task, therefore improving the performance of dataset distillation. Deep neural networks achieve remarkable perfo... read more 
Computer Vision and Pattern Recognition cs.AI

Beyond native sequence recovery: Improved modeling of thesequence-energy landscape of protein structures

bioRxiv Jan 15, 2026
Computational protein design using machine learning models has advanced rapidly since the introduction of AlphaFold2. There is now a suite of tools that enable in silico design of proteins with desired structures and properties. Most design workflows... read more 
biophysics

Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG

bioRxiv Jan 15, 2026
Alcohol Use Disorder (AUD) is a prevalent and debilitating neuropsychiatric condition characterized by compulsive alcohol consumption, impaired control, and negative emotional states, affecting about 28 million adults in the United States. Despite it... read more 
neuroscience

The geometry of context-dependent biased decisions during learning

bioRxiv Jan 15, 2026
Adaptive behavior requires inferring latent context and rapidly adjusting decisions in response to changing environmental contingencies. We investigated how reward context is learned, represented, and updated during decision making. We recorded large... read more 
neuroscience

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

bioRxiv Jan 15, 2026
Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, ... read more 
immunology

Extending Conformational Ensemble Prediction to Multidomain Proteins and Protein Complex

bioRxiv Jan 15, 2026
Proteins execute cellular functions through a structural continuum ranging from stable, folded domains to highly dynamic intrinsically disordered regions (IDRs). Conformational ensembles represent the set of three-dimensional structures a protein ado... read more 
bioinformatics

Label-free detection of individual virus-infected cells using deep learning

bioRxiv Jan 15, 2026
Numerous applications in research and medicine rely on reliable identification and quantification of virus-infected cells. Current methods either apply reporter viruses, that often differ from clinical isolates (e. g. cell tropism, immune evasion) or... read more 
bioinformatics

MorphoLearn: A morphology-driven workflow to decipher 3D electron microscopy segmentation in diatoms

bioRxiv Jan 15, 2026
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medi... read more 
cell biology

ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort

medRxiv Jan 15, 2026
BackgroundThe ATN (Amyloid/Tau/Neurodegeneration) framework provides a theory-driven approach to Alzheimers disease (AD) classification using binary biomarker cutoffs, while unsupervised machine learning offers data-driven phenotyping. The concordanc... read more 
neurology
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