HOPE, a transformer-based AI model, predicts hyperprogressive disease risk in advanced liver cancer patients on PD-1 inhibitors by combining CT imaging with clinical factors.

A mixed-methods study of 930 Chinese medical students reveals significant GenAI dependence linked to reduced critical thinking, raising urgent questions about responsible integration in medical education.

A systematic review of large language models in clinical practice finds promising early results but reveals alarming gaps in evidence quality and reporting standards that must be addressed…

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.