UCLA researchers have developed an AI system that turns fragmented electronic health records (EHR) normally in tables into readable narratives, allowing artificial intelligence to make sense of complex patient histories and use these narratives to perform clinical decision support with high accuracy.

Mayo Clinic researchers have developed a new artificial intelligence (AI) tool that helps clinicians identify brain activity patterns linked to nine types of dementia, including Alzheimer's disease, using a single, widely available scan - a transformative advance in early, accurate diagnosis.

Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications, such as cirrhosis and liver cancer, making it crucial to detect early and initiate treatment.

In radiation therapy, precision can save lives. Oncologists must carefully map the size and location of a tumor before delivering high-dose radiation to destroy cancer cells while sparing healthy tissue. But this process, called tumor segmentation, is still done manually, takes time, varies between doctors - and can lead to critical tumor areas being overlooked.

Academic medical centers could transform patient care by adopting principles from learning health systems principles, according to researchers from Weill Cornell Medicine and the University of California, San Diego. In this approach, information from electronic health records, clinical trials and day-to-day hospital operations is analyzed in real-time to uncover insights that continuously improve patient care.

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within tumours, opening doors for more targeted therapies for patients.

Findings on the development and use of the AI tool, called AAnet, have today been published in Cancer Discovery, a journal of the American Association for Cancer Research.

Diagnostic errors are among the most serious problems in everyday medical practice. AI systems - especially large language models (LLMs) like ChatGPT-4, Gemini, or Claude 3 - offer new ways to efficiently support medical diagnoses. Yet these systems also entail considerable risks - for example, they can "hallucinate" and generate false information.