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.

University researchers have pioneered a new tool to determine the risk of secondary heart attacks in cancer patients using Artificial Intelligence (AI).

Cancer patients who suffer a heart attack face increased risks because of their weakened cardiovascular system. This means they are more likely to die, bleed or experience another serious cardiovascular event.

Prediabetes is an extremely heterogeneous metabolic disorder. Scientists from several partner institutes of the German Center for Diabetes Research (DZD) have now used artificial intelligence (AI) to identify epigenetic markers that indicate an elevated risk of complications. A simple blood test could be sufficient to identify individuals at high risk of developing type 2 diabetes and its complications at an early stage. The study shows how data-driven approaches and molecular medicine interact in the diagnostic process.

A new medical large language model (LLM) achieved over 91 percent accuracy in identifying female participants diagnosed with major depressive disorder after analyzing a short WhatsApp audio recording where participants described their week, according to a study published in the open-access journal PLOS Mental Health by Victor H. O. Otani, from Santa Casa de São Paulo School of Medical Sciences and Infinity Doctors Inc., Brazil, and colleagues.