Rapid advances in AI technology have made these considerations critical across the industry, with public and private stakeholders rushing to catch up, as evidenced by guiding principles for ML-enabled devices recently issued by FDA Center for Devices and Radiological Health (CDRH).
It’s time to put that technology to work widely — in ways that prioritize conscientious protocols designed to prevent bias in data gathering and use in patient care, said Nigam Shah, MBBS, PhD, a Stanford Medicine professor of biomedical data science and of medicine.
As artificial intelligence changes the way medicine is practiced, humans become more beholden to algorithms — making it crucial to get those machine-human collaborations correct at the outset.
