A recent Special Communication in the Journal of the American Medical Association (JAMA) highlights the FDA's evolving approach to regulating AI in healthcare. The article explores AI's growing role in clinical research, medical product development, and patient care, while addressing the unique regulatory challenges posed by AI in biomedicine and healthcare.

Regulating AI in Medicine The FDA's regulation of AI-enabled medical devices dates back to 1995 with the approval of PAPNET, an AI-based tool used by pathologists for cervical cancer diagnosis. Although PAPNET was not widely adopted due to high costs, the FDA has since approved nearly 1,000 AI-driven medical devices, particularly in radiology and cardiology.

AI is now playing a significant role in drug development, from drug discovery and clinical trials to dosage optimization. In addition to its widespread use in oncology, AI applications in mental health are gaining traction, with digital technologies offering substantial potential for impact in this field.

Over the past year, the number of regulatory submissions for AI in drug development has increased tenfold. To accommodate the complexities and wide-ranging applications of AI, the FDA has adapted its regulatory framework to be risk-based, while also considering AI's real-world evolution in clinical practice.

A Strategic Approach to AI Regulation In 2021, the FDA introduced a five-point plan to regulate AI and machine learning-based medical devices. This plan seeks to foster innovation while ensuring the safety and efficacy of AI-driven products. It aligns with Congressional guidance urging the FDA to develop flexible regulations that allow AI developers to update their products without requiring continuous approval for every modification.

However, the article emphasizes the importance of managing AI products throughout their entire lifecycle, particularly through ongoing performance monitoring after deployment in clinical settings.

The FDA’s medical products center has identified four key areas of focus for AI development: enhancing public health safety, supporting regulatory innovation, promoting best practices and harmonized standards, and advancing research for evaluating AI performance.