Using machine learning, an electronic nose can “smell” early signs of ovarian cancer in the blood. The method is precise and, according to the LiU researchers behind the study, it could eventually be used to find many different cancers. The study is published in the scientific journal Advanced Intelligent Systems.

As members of the public increasingly turn to AI with health concerns, University of Birmingham researchers are leading a global programme to build the first definitive guide for safely navigating health information on AI powered chatbots.

The initiative is announced in a correspondence published in Nature Health.

A world-first review led by Adelaide University researchers has found there’s a lack of clear guidelines around the early testing of AI tools in health clinics, during a process known as silent trials.

The global scoping review looked at this early phase of testing and revealed huge variations in the way the trials are being conducted and the measures used to assess the effectiveness of the tools.

A new artificial intelligence-driven pipeline developed in a collaborative research combines protein structure prediction, sequence design, and live-cell screening together to enable rapid conversion of antibody sequences into functional intracellular antibodies (intrabodies) that are stable within living cells. By preserving antigen-binding regions and improving structural stability, the approach overcomes major barriers encountered in intrabody development - emerging as a simpler, more cost-effective tool for diagnostics, imaging, and biomedical research.

In 2024, approximately 200,000 people in Germany received artificial hip joints, making this operation one of the most common orthopedic procedures in German hospitals. In most cases, such operations are performed to treat hip osteoarthritis, which is the result of wear on the cartilage surfaces of the femoral (thigh bone) head and the hip socket. In terms of mobility and freedom from pain, patients react differently to total hip replacement.

Researchers from the University of Minnesota Medical School, in collaboration with Emory University and the Georgia Institute of Technology, have developed a new artificial intelligence (AI) biomarker tool that may help predict how ovarian cancer patients will respond to treatment at the time of diagnosis. The findings were published in the British Journal of Cancer ReportsExternal link that opens in the same window.

An AI-powered model developed at University of Michigan can read a brain MRI and diagnose a person in seconds, a study suggests.

The model detected neurological conditions with up to 97.5% accuracy and predicted how urgently a patient required treatment.