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.

Diagnosing substance-use disorder can be difficult because of patient denial related to the stigma attached to addiction.

But a new study by the University of Cincinnati uses a novel artificial intelligence to predict substance use defining behaviors with up to 83% accuracy and 84% accuracy to predict the severity of the addiction. Researchers say this could allow clinicians to provide treatment faster to patients who need it.

Mass General Brigham investigators have developed a robust new artificial intelligence (AI) foundation model that is capable of analyzing brain MRI datasets to perform numerous medical tasks, including identifying brain age, predicting dementia risk, detecting brain tumor mutations and predicting brain cancer survival. The tool. known as BrainIAC, outperformed other, more task-specific AI models and was especially efficient when limited training data were available.