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.

A new study shows that AI-based image reconstruction for breast tomosynthesis maintains cancer detection rates while improving specificity and reducing the number of images radiologists must interpret.

Psychiatry's recent trajectory: objective biomarkers emerging for detection, mechanistic bridges from neurons to clinical phenotype, and validated AI systems entering practice with governance frameworks in parallel.

MCG2Vec, a self-supervised learning model, detects multiple cardiac pathologies from magnetocardiography with interpretable patterns aligned to cardiac physiology.

One in five US adolescents use AI chatbots for mental health advice, but most keep it secret. A survey reveals how embedded these tools are in youth mental…

Electronic health records power medical research and AI systems, but conventional validation tools miss semantic inconsistencies. A new framework borrows from software testing to automatically detect when data…

Children who visit the ED for asthma often return. A new machine learning model can identify which ones will, enabling earlier intervention with specialized care.

This week's papers show where AI actually helps: stratifying cancer patients, decoding impossible diseases, guiding surgery, monitoring hearts—and uncovering the awkward truth that algorithms help less experienced clinicians…