Regulatory Landscape and Cross-Market Diffusion of AI-Enabled Medical Devices: A Comparative Analysis of USA and Australia

Rationale and Objectives:Artificial intelligence (AI) enabled medical devices are transforming healthcare delivery, with regulatory frameworks shaping their adoption and integration into clinical systems. The 2025 release of the Australian Therapeutic Goods Administration (TGA) AI devices list provides an opportunity to evaluate the national AI ecosystem and benchmark it against the United States Food and Drug Administration (FDA). This study aimed to characterise AI-enabled medical devices across both jurisdictions, focusing on specialty distribution, functional roles, and cross-market diffusion.Materials and Methods:A cross-sectional analysis of publicly available FDA and TGA AI-enabled medical device lists was conducted as of December 2025. Devices were categorised by clinical specialty, primary AI function, regulatory classification, and manufacturer origin. Overlapping devices were identified to assess regulatory lag. Descriptive statistics summarised distributions and trends.Results:A total of 1,357 AI-enabled devices were identified in the FDA database and 125 in the TGA database. Radiology represented the largest specialty in both jurisdictions (FDA: 76.5%; TGA: 44.8%). FDA devices were predominantly focused on image and signal interpretation, whereas TGA-listed devices showed a broader distribution across measurement, workflow, and treatment-support applications. Among 77 devices approved in both jurisdictions, the median time from FDA clearance to TGA inclusion was 559 days, indicating a consistent regulatory lag. The United States was the primary source of devices in Australia, with a smaller contribution from domestic manufacturer.Conclusion:AI-enabled medical devices are concentrated in radiology, highlighting its central role in digital health. Differences in functional adoption and regulatory lag reflect variation in regulatory pathways and market dynamics. These findings inform health system planning, regulatory alignment, and the integration of AI into clinical workflow.