In India, it is difficult to track regulatory approvals for many products. Much worse are the AI/ML enabled algorithms. Around the world, things are not great either. However, recently, US FDA decided to publish a list of the AI/ML enabled medical devices (or algorithms) that it has approved by category. It is interesting to browse the list as it shows some interesting patterns.
Interest in medical devices incorporating ML functionality has increased in recent years. Over the past decade, the FDA has reviewed and authorized a growing number of devices legally marketed (via 510(k) clearance, granted De Novo request, or approved PMA) with ML across many different fields of medicine—and expects this trend to continue.
The FDA is providing this initial list of AI/ML-enabled medical devices marketed in the United States as a resource to the public about these devices and the FDA’s work in this area.
On October 14, 2021, FDA’s Digital Health Center of Excellence (DHCoE) held a public workshop on the transparency of artificial intelligence/machine learning-enabled medical devices. The workshop followed the recently published list of nearly 350 AI/ML-enabled medical devices that have received regulatory approval since 1997. The workshop was aimed at moving forward the objectives of FDA’s DHCoE to “empower stakeholders to advance healthcare by fostering responsible and high-quality digital health innovation.” The DHCoE was established in 2020 within FDA’s Center for Devices and Radiological Health (CDRH) under Bakul Patel.
This initial list contains publicly available information on AI/ML-enabled devices. The FDA assembled this list by searching FDA’s publicly-facing information, as well as by reviewing information in the publicly available resources cited below and in other publicly available materials published by the specific manufacturers.
This list is not meant to be an exhaustive or comprehensive resource of AI/ML-enabled medical devices. Rather, it is a list of AI/ML-enabled devices across medical disciplines, based on publicly available information.
If grouped by category, this is what we see.
Radiology 241
Cardiovascular 41
Hematology 13
Neurology 12
Ophthalmic 6
Chemistry 5
Surgery 5
Microbiology 5
Anesthesia 4
GI-Urology 4
Hospital 3
Dental 1
Ob/Gyn 1
Orthopedic 1
Pathology 1
Radiology is no surprise with almost 70% share of listed devices in that area as most of the AI work in healthcare and indeed in medical imaging has been primarily around chest X-rays and there are many algorithms and solutions available. What is surprising is the last in the list, pathology! Considering that too in some ways is also imaging based (whole slide scans for example), it is intriguing that it does not list as many as it should.
What is also visible from the list is that other than radiology really, there are not many solutions in other areas. Radiology is the so to speak, low hanging fruit in healthcare and imaging.
There is so much scope to do in healthcare. The need of the hour is for computer science community to engage with medical fraternity and help deploy some of the algorithms, not as to replace those in there, but to aid them in making decision, the proverbial second opinion. It does not harm. Can it bias the practitioner to just go with the AI prediction? It may, but if there is uncertainty, there is anyways a dilemma the practitioners face.
It is time, given the scale and scarcity of resources we have in India and population so widely spread geographically, that such solutions will only help provide better healthcare. How to achieve that is a different question though.
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