The real-world impact of artificial intelligence ethics frameworks across a decade in healthcare: a scoping review

The number of ethical frameworks designed to guide artificial intelligence (AI) use has grown substantially over the past decade, yet their real-world effect remains unclear. We aimed to synthesize existing evidence to analyze the practical impact of AI ethics frameworks (AIEFs) operationalized in healthcare.

We conducted a scoping review across 4 academic databases (Ovid MEDLINE, Ovid Embase, Scopus, and Web of Science), Google, and Google Scholar from January 2014 to January 2025. Eligible studies reported primary research on the qualitative or quantitative impacts of AIEFs implemented in healthcare. Data synthesis was conducted via narrative review.

Of 1807 records identified, 16 studies met inclusion criteria. These comprised 5 preliminary initiatives testing guidelines in practice, 5 case studies, 5 implementation studies, and a comparative case study. AIEFs were implemented: (1) to develop new AI governance structures and guidelines, (2) as ethical review assessment systems for adopting clinical AI technologies, and (3) as ethical "audit" tools for identifying ethical risks. Impact was reported through qualitative improvements to process measures such as improved trust in AI. No studies demonstrated a direct link between AIEFs and health-related outcome measures such as patient safety.

AIEFs led to changes in organizational or clinical processes, including increased compliance with ethical standards. When embedded in governance, AIEFs improved oversight and evaluation, but audits were constrained by their reliance on organizational cooperation.

Despite the proliferation of AIEFs over the past decade, their implementation in healthcare remains limited and impact on health outcomes unmeasured or underreported.