An 'AI scientist', working in collaboration with human scientists, has found that combinations of cheap and safe drugs - used to treat conditions such as high cholesterol and alcohol dependence - could also be effective at treating cancer, a promising new approach to drug discovery.

A University of Maine study compared how well artificial intelligence (AI) models and human clinicians handled complex or sensitive medical cases.

The study published in the Journal of Health Organization and Management in May evaluated more than 7,000 anonymized medical queries from the United States and Australia.

As artificial intelligence (AI) continues to be integrated in healthcare, a new multinational study involving Aarhus University sheds light on how dental patients really feel about its growing role in diagnostics. The verdict? Patients are cautiously optimistic welcoming the potential benefits of AI but drawing a firm line: humans must stay in charge.

A new study by investigators from Mass General Brigham showed that a new app they created can help improve the quality of life for caregivers of patients undergoing bone marrow transplant (BMT). The researchers conducted a randomized clinical trial and found that caregivers assigned to use the app showed significantly greater improvements in quality of life, burden, and mood symptoms compared to those who did not have the app.

A potentially lifesaving new smartphone app can help people determine if they are suffering heart attacks or strokes and should seek medical attention, a clinical study suggests.

The ECHAS app (Emergency Call for Heart Attack and Stroke) is being developed by experts at UVA Health, Harvard, Northeastern and other leading institutions.

A new telemedicine service for personalised breast cancer prevention has launched at preventcancer.co.uk. It allows women aged 30 to 75 across the UK to understand their risk of developing breast cancer and take early action years before NHS screening begins.

The service delivers a personalised breast cancer prevention plan based on each woman’s genetic profile using a simple home saliva test and online clinical guidance.

Scientists aiming to advance cancer diagnostics have developed a machine learning tool that is able to identify metabolism-related molecular profile differences between patients with colorectal cancer and healthy people.

The analysis of biological samples from more than 1,000 people also revealed metabolic shifts associated with changing disease severity and with genetic mutations known to increase the risk for colorectal cancer.