A prospective study reveals that freely available AI models significantly underperform dermatologists in diagnosing nail disorders, raising important questions about AI's role in clinical dermatology.

A deep learning approach reveals the genetic architecture of brain white matter with unprecedented detail, uncovering thousands of previously unknown genetic variants and potential drug targets.

AI foundation models support less experienced clinicians but expert dermatologists outperform all algorithms in skin lesion diagnosis.

Medical imaging AI models degrade in performance after deployment. ReclAIm—a multi-agent framework—automates detection and correction of this decline while preventing catastrophic forgetting.

Recent Anesthesiology & Critical Care coverage reveals a field in transition: static risk calculators are giving way to dynamic trajectory models that predict deterioration hours ahead, reinforced by…

Researchers developed MCEN, a Mamba-based deep learning model that accurately predicts chemotherapy response in breast cancer using pre-treatment needle biopsies, offering a clinical decision-support tool for personalized treatment.

Contactless smart home sensors combined with AI can identify individuals at risk of cerebrovascular disease by detecting subtle behavioral changes, potentially enabling earlier intervention.

An AI framework automatically analyzes cardiac MRI to identify patterns that better predict major complications after heart attacks than conventional methods.

SKELEX demonstrates how foundation models solve a critical bottleneck in medical AI: the need to retrain from scratch for each new disease or anatomical region. This self-supervised approach…

Researchers developed a proteomics-based machine learning model that predicts diabetic retinal neurodegeneration from plasma proteins, enabling early risk stratification via a simple blood test.