FDA highlights the need to address bias in AI

At a meeting of the agency's Patient Engagement Advisory Committee, officials stressed the necessity of ensuring there's diversity in the data used to train algorithms.

The U.S. Food and Drug Administration on Thursday convened a public meeting of its Center for Devices and Radiological Health's Patient Engagement Advisory Committee to discuss issues regarding artificial intelligence and machine learning in medical devices.

"Devices using AI and ML technology will transform healthcare delivery by increasing efficiency in key processes in the treatment of patients," said Dr. Paul Conway, PEAC chair and chair of policy and global affairs of the American Association of Kidney Patients.

As Conway and others noted during the panel, AI and ML systems may have algorithmic biases and lack transparency – potentially leading, in turn, to an undermining of patient trust in devices. 

Medical device innovation has already ramped up in response to the COVID-19 crisis, with CDRH Director Dr. Jeff Shuren noting that 562 medical devices have already been granted emergency use authorization by the FDA.