St. Thomas Law Review
First Page
153
Document Type
Comment
Abstract
The integration of artificial intelligence (Al) in medical devices, particularly the subset of Al technologies known as machine learning, has sparked a new era of precision and efficiency in healthcare. AI/ML-enabled medical devices are proving to be invaluable as they have already improved patient diagnosis, treatment, and disease prediction. As machine learning continues to be adopted in medical devices, the U.S. Food and Drug Administration (FDA) continues to receive more marketing submissions and pre-submissions for AI/ML-enabled medical devices, a trend that is expected to increase over time. While the FDA has made significant progress in proposing regulatory frameworks that will implement the use of AI/ML-enabled medical devices, it has not considered whether these devices should be monitored based on the level of risk they pose. Therefore, this Comment aims to advance conversations that will promote the safe use of machine learning in healthcare and argues that the FDA should adopt a risk-based approach to the monitoring of AI/ML-enabled medical devices. Adopting such an approach is warranted for several reasons and will provide significant benefits to manufacturers, patients, and the FDA. By tailoring monitoring requirements to device risk levels, the FDA can strike a balance between ensuring patient safety and fostering efficiency in the rapidly evolving field of machine learning in healthcare.
Recommended Citation
Cristian A. Rodriguez,
An Artificially Intelligent Approach to Medical Device Monitoring,
36
St. Thomas L. Rev.
153
(2024).
Available at:
https://scholarship.stu.edu/stlr/vol36/iss2/5