FDA whitepaper floats ideas for new AI regulatory framework

With the exploratory paper, the FDA takes another step toward catching up with the pace of machine learning innovation.

The US Food and Drug Administration (FDA) “is taking new steps to develop a modern regulatory framework to make sure that safe and effective artificial intelligence devices can efficiently advance to patients.”

So tweeted outgoing Commissioner Dr. Scott Gottlieb this week as the agency released a 20-page exploratory whitepaper on how it could address artificial intelligence and machine learning algorithms.

“As algorithms evolve, the FDA must also modernize our approach to regulating these products,” Gottlieb wrote in a statement. “We must ensure that we can continue to provide a gold standard of safety and effectiveness. We believe that guidance from the agency will help advance the development of these innovative products.”

The FDA’s current problem is largely procedural, as, since the agency, generally requires manufacturers to resubmit clearance when major modifications are made to software, it is ill-equipped to handle algorithms that constantly learn and self-improve,

According to the proposed new framework, companies could include in their premarket submissions for AI products their plans for anticipated modifications. This includes what the agency has dubbed “SaMD Pre-Specifications” (SPS) and “algorithm change protocol” (ACP).

The SPS lays out the sorts of changes the company anticipates making, while the ACP details the processes they will follow to make those changes.

Certain changes, like improvements in performance and changes in data inputs, would not require a new submission, but others, like significant changes to intended use, would.

While the FDA has been approving AI algorithms for some time, these algorithms have been viewed as limited in their ability to learn. 

As Gottlieb explained in his statement, “The artificial intelligence technologies granted marketing authorization and cleared by the agency so far are generally called ‘locked’ algorithms that don’t continually adapt or learn every time the algorithm is used. These locked algorithms are modified by the manufacturer at intervals, which includes ‘training’ of the algorithm using new data, followed by manual verification and validation of the updated algorithm.”

Machine-learning algorithms, by contrast, “often called ‘adaptive’ or ‘continuously learning’ algorithms, don’t need manual modification to incorporate learning or updates,” Gottlieb wrote. 

The whitepaper is not yet a draft guidance, and the FDA is seeking comments on it before moving further.