Any digital health conference features its share of machine learning evangelism. Technology executives give fervent testimonials about its power to save lives and money, to predict episodes of severe illness, to help hospitals root out inefficiency.
This year’s gathering of the Health Information Management Systems Society (HIMSS) in Las Vegas was no different. But in between the glowing anecdotes, an aggressive counter narrative emerged: Machine learning needs a watchdog.
Throughout the four-day conference, the largest annual event in health care technology, industry leaders called for better ways to evaluate the usefulness of machine learning algorithms, audit them for bias, and put in place regulations designed to ensure reliability, fairness, and transparency.
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