Researchers at Weill Cornell Medicine have used machine learning to define three subtypes of Parkinson's disease based on the pace at which the disease progresses. In addition to having the potential to become an important diagnostic and prognostic tool, these subtypes are marked by distinct driver genes. If validated, these markers could also suggest ways the subtypes can be targeted with new and existing drugs.

A new study by researchers from the Psychology Department at the Hebrew University have made significant strides in understanding the role of artificial intelligence (AI) in mental health therapy. Their research focuses on the delicate balance between AI-driven interactions and the irreplaceable human touch in therapeutic settings, addressing critical questions about when AI might effectively replace human therapists and when the human connection remains indispensable.

As part of a nationwide trend, many more of NYU Langone Health's patients during the pandemic started using electronic health record tools to ask their doctors questions, refill prescriptions, and review test results. Many patients’ digital inquiries arrived via a communications tool called In Basket, which is built into NYU Langone’s electronic health record (EHR) system, EPIC.

Monitoring of heart rate and physical activity using consumer wearable devices was found to have clinical value for comparing the response to two treatments for atrial fibrillation and heart failure.

The study published in Nature Medicine examined if a commercially-available fitness tracker and smartphone could continuously monitor the response to medications, and provide clinical information similar to in-person hospital assessment.

Cambridge scientists have developed an artificially intelligent (AI) tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer's disease.

The team say this new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best.

Researchers have developed a groundbreaking method for analysing heart MRI scans with the help of artificial intelligence (AI), which could save valuable NHS time and resources, as well as improve care for patients.

The teams from the Universities of East Anglia (UEA), Sheffield and Leeds created an intelligent computer model that utilises AI to examine heart images from MRI scans in a specific view known as the four-chamber plane.

Children and young people are generally positive about artificial intelligence (AI) and think it should be used in modern healthcare, finds the first-of-its-kind survey led by UCL and Great Ormond Street Hospital (GOSH).

The national study asked children and young people aged six to 23 years old, across all four UK nations for their views on how they would like AI to be used to enhance their healthcare.