Scientists at the Icahn School of Medicine at Mount Sinai have developed a novel artificial intelligence (AI) tool that not only identifies disease-causing genetic mutations but also predicts the type of disease those mutations may trigger.

The method, called V2P (Variant to Phenotype), is designed to accelerate genetic diagnostics and aid in the discovery of new treatments for complex and rare diseases.

Reduced coronary blood flow, measured with an artificial intelligence-based imaging tool, predicted future cardiovascular events in patients with suspected stable coronary artery disease.(1) These findings were presented at EACVI 2025, the flagship congress of the European Association of Cardiovascular Imaging (EACVI), a branch of the European Society of Cardiology (ESC).

While targeted radiation can be an effective treatment for brain tumours, subsequent potential necrosis of the treated areas can be hard to distinguish from the tumours on a standard MRI. A new study led by a York University professor in the Lassonde School of Engineering found that a novel AI-based method is better able to distinguish between the two types of lesions on advanced MRI than the human eye alone, a discovery that could help clinicians more accurately identify and treat the issues.

Balance training patients may soon be able to get AI feedback during home exercises, with four wearable sensors and a new machine learning model developed at the University of Michigan.

The team hopes their technology could help patients make faster progress during physical therapy and maintain their abilities after the end of their prescribed sessions.

Sweat contains a wealth of biological information that, with the help of artificial intelligence and next-generation sensors, could transform how we monitor our health and wellbeing, a new study suggests.

The study, published in the Journal of Pharmaceutical Analysis, examines sweat's potential for real-time monitoring of hormones and other biomarkers, medication doses, and early detection of diseases such as diabetes, cancer, Parkinson's and Alzheimer's.

While artificial intelligence (AI) technology is increasingly being used - formally and informally - to support medical diagnoses, its utility in emergency medical settings remains an open question. Can AI support doctors in situations where split-second decision making can mean the difference between life and death? Researchers at Drexel University broached the question with clinicians at Children’s National Medical Center in Washington, D.C., to better understand how and when the technology could help them save lives.

A new study shows that an AI assistant can conduct assessment conversations with patients with higher accuracy than the rating scales used in healthcare today. In the study, 303 participants were interviewed by the AI ​​assistant Alba, who then suggested possible psychiatric diagnoses.

In addition to being interviewed by an AI assistant, the participants also had to fill out standardized rating scales for the nine most common psychiatric diagnoses.