Distinguishing between Rigor and Transparency in FDA Marketing Authorization of AI-enabled Medical Devices

Abstract

The increasing prevalence of artificial intelligence (AI)-enabled medical devices presents significant opportunities for improving patient outcomes. However, recent studies based on public U.S. Food and Drug Administration (FDA) summaries have raised concerns about the extent of validation that such devices undergo before FDA marketing authorization and subsequent clinical deployment. Here, the authors clarify key concepts of FDA regulation and provide insights into the current standards of performance validation, focusing on radiology AI devices. The authors distinguish between two fundamentally different but often conflated concepts: validation rigor (ie, the quality and comprehensiveness of the evidence supporting a device's performance) and validation transparency (ie, the extent to which this evidence is publicly accessible). The authors begin by describing the inverse relationship between the amount of performance data contained and the transparency of specific components of an FDA submission. Drawing on FDA guidelines and on experience developing authorized AI devices, the authors outline current validation standards and present a mapping from common radiology AI device types to their typical clinical study designs. This article concludes with actionable recommendations, advocating for a balanced approach tailored to specific use cases while still enforcing certain universal standards. These measures will help ensure that AI-enabled medical devices are both rigorously evaluated and transparently reported, thereby fostering greater public trust and enhancing clinical utility.

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