Regulating Generative AI in Radiology Practice: A Trilaminar Approach to Balancing Risk with Innovation.
Authors
Affiliations (2)
Affiliations (2)
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.G., B.C.B., K.J.D.); Harvard Medical School, Boston, Massachusetts (V.G., B.C.B., K.J.D.). Electronic address: [email protected].
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.G., B.C.B., K.J.D.); Harvard Medical School, Boston, Massachusetts (V.G., B.C.B., K.J.D.); Mass General Brigham AI, Boston, Massachusetts (B.C.B., K.J.D.).
Abstract
Generative AI tools have proliferated across the market, garnered significant media attention, and increasingly found incorporation into the radiology practice setting. However, they raise a number of unanswered questions concerning governance and appropriate use. By their nature as general-purpose technologies, they strain the limits of existing FDA premarket review pathways to regulate them and introduce new sources of liability, privacy, and clinical risk. A multilayered governance approach is needed to balance innovation with safety. To address gaps in oversight, this piece establishes a trilaminar governance model for generative AI technologies. This treats federal regulations as a scaffold, upon which tiers of institutional guidelines and industry self-regulatory frameworks are added to create a comprehensive paradigm composed of interlocking parts. Doing so would provide radiologists with an effective risk management strategy for the future, foster continued technical development, and ultimately, promote patient care.