A new study using machine learning is the first of its kind to comprehensively characterize how proteoglycans play a role in breast cancer prognosis and tumor aggressiveness.

AI puts doctors in a bind, says Shefali Patil, associate professor of management at Texas McCombs, in a recent article. Health care organizations are increasingly pushing them to rely on assistive AI to minimize medical errors. But they lack direct support for how to use it.

The result, Patil says, is that physicians risk burnout, as society decides whom to hold accountable when AI is involved in medical decisions. Paradoxically, they also face greater chances of making medical mistakes.