The U.S. FDA may be the most advanced regulatory agency when it comes to artificial intelligence (AI) and machine learning (ML), but developers of these products still have little in the way of FDA guidance to work with in many instances. Cassie Scherer of Dublin-based Medtronic plc, told attendees at this year’s Food and Drug Law Institute annual conference that they should have a product change control protocol ready to go despite the absence of FDA guidance on the subject, an effort that will increase time to market but pay eventually big dividends.

The U.S. FDA is among the regulators that are taking account of the views of patients in medical device development and regulation, but artificial intelligence (AI) and machine learning (ML) are terra incognita for many, if not most patients. Rebekah Angove, vice president for patient experience and program evaluation at the Patient Insight Institute, told BioWorld that while some patients clearly want to know more about AI and ML, it is also clear that more than a certain amount of detail is more of a distraction than a help for most patients.

The U.K. Medicines and Healthcare Products Regulatory Agency (MHRA) is still considering a drastic overhaul of the 2002 medical device regulatory framework, but may have sent a signal that its new framework won’t deviate too far from established regulatory approaches. The agency reported June 16 that it has signed on with both the International Council for Harmonization (ICH) for drugs and the International Medical Device Regulators Forum (IMDRF) for devices, providing members of both industries with at least some modest confidence that access to the 67 million strong U.K. market won’t suffer from a new set of unique regulatory hurdles.

The EU’s Medical Device Coordination Group (MDCG) has advised the device industry that many manufacturers seem ill prepared for the transition to the Medical Device Regulation (MDR), which will be fully in force for all devices as of May 2024. MDCG said that that any leniency shown after that date will be granted only for devices that address an urgent public health need, potentially leaving many existing authorizations out in the cold.

Developers of artificial intelligence (AI) and machine learning (ML) algorithms in medical radiology tend to think of regulatory approval as the primary hurdle to market, but there is also the question of how to pay for the use of these products. Public and private payers obviously hold the purse strings, but appealing to payers is still not always as straightforward proposition as some would like, reinforcing the notion that coverage and reimbursement still combine to serve as one of the highest hurdles to market for AI and ML.