Ethical and Regulatory Issues in Governing AI-Enabled Software as a Medical Device: A Scoping Review.
TL;DR
This scoping review examined the ethical and regulatory issues surrounding AI-enabled SaMD across jurisdictions and across the product lifecycle, drawing together peer-reviewed literature, standards, and official guidance.
Abstract
Purpose
Artificial intelligence (AI)-enabled software as a medical device (SaMD) is increasingly used across clinical specialties, but its governance remains difficult because adaptive systems raise ongoing concerns about version control, subgroup performance reporting, and postmarket performance drift. This scoping review examined the ethical and regulatory issues surrounding AI-enabled SaMD across jurisdictions and across the product lifecycle, drawing together peer-reviewed literature, standards, and official guidance.
Methods
A scoping review was conducted using the Joanna Briggs Institute framework and reported in accordance with PRISMA-ScR. Sources published between 2015 and 2025 were identified from PubMed, PubMed Central, Google Scholar, medRxiv/bioRxiv, and the Cochrane Library. Included records were charted by lifecycle stage, jurisdiction, and six prespecified themes: transparency, equity, privacy and security, accountability, lifecycle oversight and change control, and convergence versus fragmentation. Coding was refined iteratively during reviewer calibration.
Findings
Of 21,672 records identified, 369 met the inclusion criteria. Most sources were published from 2019 onward and were concentrated in United States and European Union regulatory settings, with additional contributions from international bodies such as International Medical Device Regulators Forum, WHO, and ISO, as well as from emerging economies including China and India. Postmarket oversight emerged as the most strongly emphasized lifecycle stage, especially in relation to real-world monitoring, drift management, and prespecified change control. Common gaps included limited subgroup reporting, inconsistent expectations for drift thresholds and rollback criteria, and poor alignment between horizontal AI rules and device-specific regulatory frameworks. IMPLICATIONS The evidence base shows meaningful progress in the governance of AI-enabled SaMD, but implementation remains uneven across jurisdictions and lifecycle stages. Priority areas include standardized equity reporting, clearer minimum expectations for drift management, and more explicit integration between horizontal AI governance frameworks and SaMD-specific regulatory requirements. These findings support stronger accountability through improved reporting standards, clearer postmarket controls, and better alignment of quality-management processes across the AI-SaMD lifecycle.