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.

A type of artificial intelligence (AI) called fine-tuned large language models (LLMs) greatly enhances error detection in radiology reports, according to a new study published in Radiology, a journal of the Radiological Society of North America (RSNA). Researchers said the findings point to an important role for this technology in medical proofreading.

A joint research team from The Hong Kong University of Science and Technology and The Hong Kong University of Science and Technology (Guangzhou) has published a perspective article in MedComm - Future Medicine. The article comprehensively evaluates DeepSeek-R1, a Chinese-developed open-source large language model (LLM), and its potential to transform the healthcare landscape.

A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research published at the ATS 2025 International Conference.

The model, called Sybil, which was originally developed using National Lung Screening Trial (NLST) data by investigators from the Massachusetts Institute of Technology and Harvard Medical School, could be used to guide more personalized lung cancer screening strategies.