From Regulatory Authorization to Clinical Accountability: A Control Preventability Framework for Healthcare AI

Vollständiger Abstract

Worum geht es in dieser Arbeit?

The integration of artificial intelligence into healthcare has accelerated dramatically, with the FDA authorizing 1,430 AI/ML-enabled medical devices by the end of 2025 including 331 authorizations in that year alone yet the evidentiary foundation for many deployed tools remains limited, and accountability frameworks have not kept pace with technological adoption. This integrative evidence–regulatory analysis synthesizes FDA authorization data, selected high-relevance clinical evidence from randomized trials and systematic reviews, and primary regulatory and governance materials from the FDA, European Union, and World Health Organization. The analysis reveals that among 1,357 FDA-cleared AI devices identified through December 2025, only 2.5% were linked to registered prospective trials and only 0.2% had identified evidence evaluating patient-centered outcomes. A randomized clinical trial involving 44 physicians found that exposure to erroneous large language model recommendations reduced diagnostic reasoning accuracy by 14.0 percentage points (95% CI, −18.9 to −9.1; P