One of the fastest-growing segments of the medical technology market is that of artificial intelligence. Artificial intelligence/machine learning (AI/ML) has wide-ranging applications within the healthcare industry--from its use in automating internal business performance, improving health analytics, or incorporation within medical devices. With 80 percent of healthcare executives planning to adopt AI tools (1) and the global AI in healthcare market projected to grow upwards of 40 percent from 2021-2028, (2) there is an ever-increasing need for clarity on how to achieve U.S. market access for medical devices incorporating AI algorithms.
In the United States, the U.S. Food and Drug Administration (FDA) pathway for device marketing authorization relies on the demonstration of safety and effectiveness of the final device design (regardless of a product's classification). This presents an obvious problem for AI/ML technologies that are designed to learn and adapt over time in response to real-world data. How can a medical device manufacturer remain in regulatory compliance when its product is constantly evolving and changing in real time?
The FDA has proposed a new regulatory framework for AI/ML devices following a total product lifecycle (TPLC) approach. (3,4) The FDA has long advocated for a total product lifecycle approach for medical devices. It is the basis for FDA's Software Precertification Pilot Program and certain device-specific FDA Guidance (e.g., infusion pumps). The framework for AI-based devices centers on four pillars:
* Good machine learning practices (GMLP)
* Premarket review for safety and effectiveness
* Established pre-specifications and algorithm change protocol
* Patient-focused transparency and real-world performance monitoring
These requirements may seem familiar, as they are essentially analogous to standard requirements such as good software engineering and quality system practices. In fact, FDA has stated it...