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.

When physicians don’t have to type detailed clinical notes while simultaneously talking to their patients, the visit feels different. Eye contact lasts longer, follow-up questions become sharper, and - crucially - clinicians go home less drained.

That’s the promise of ambient clinical documentation, an artificial intelligence (AI)-assisted technology that records healthcare conversations and transforms them into clear,

An AI tool that can analyse abnormalities in the shape and form of blood cells, and with greater accuracy and reliability than human experts, could change the way conditions such as leukaemia are diagnosed.

Researchers have created a system called CytoDiffusion that uses generative AI - the same type of technology behind image generators such as DALL-E - to study the shape and structure of blood cells.

Complex digital images of tissue samples that can take an experienced pathologist up to 20 minutes to annotate could be analysed in just one minute using a new AI tool developed by researchers at the University of Cambridge.

SMMILe, a machine learning algorithm, is able not only to correctly detect the presence of cancer cells on slides taken from biopsies and surgical sections,

A new heart monitoring system combining 3D printing and artificial intelligence (AI) could transform the way doctors measure and diagnose patients' heart health.

Developed at SFU’s School of Mechatronic Systems Engineering, the system features reusable dry 3D-printed electrodes embedded in a soft chest belt - the folding origami-shaped design uses gentle suction to stick to the skin.