10 Principles for Machine Learning or AI Medical Devices from FDA
(Thursday, October 28, 2021)
Although FDA has approved about 343 medical devices using Artificial Intelligence (AI) and Machine Learning (ML) technologies, a much larger number of devices using such technologies are used in general health awareness applications and do not require FDA’s review or approval for launch. These are mostly apps or software that collect data about real world use of a product to improve the same product’s performance. The software gets smarter with each additional use data that may help customize for individual users or a larger population of users. FDA, along with Health Canada and MHRA, released 10 “guiding principles” to highlight what the regulators would expect to form the basis of the Good Machine Learning Practice (GMLP). The regulators were careful to not call their list “Guidance Document”, “guidelines” or other regulatory terms confirming that the industry may voluntarily consider these recommendations. The preamble to the announcement states that these principles are based on lessons learnt from other technological sectors, applied to healthcare. The ten principles list common sense suggestions for any new high technology sector. These include need for multi-disciplinary expertise in product development, good quality and security practices, use of relevant representative data to train the ML or AI algorithm, measures to control bias, using clinically relevant reference datasets, measuring reliability of performance data that would be used for the learning functions, optimizing testing conditions, clear user instructions, and post deployment monitoring. The document is very precise, only 2 pages with each guiding principle described in 1-2 sentences. The document lacks details perhaps to acknowledge the generic nature of these suggestions. There are no surprises or controversial suggestions. It would hard for anyone to argue against these guidelines. At the same time, without additional guidance, most developers may have limited use for these tips. The guideline builds on a previous document released in 2019 describing the proposed regulatory framework for medical device software using AI/ML technologies. The 10 guidelines should be used as a checklist to verify completeness of the development plan for an AI/ML driven software application.
Dr. Mukesh Kumar
Founder & CEO, FDAMap
Linkedin: Mukesh Kumar, PhD, RAC