Transforming medical device clinical trials with agentic AI: a Taiwan-specific framework integrating smart data, regulatory evidence, and trial optimization

Medical device clinical trials present distinctive regulatory and methodological challenges—including device-specific classification, engineering-based endpoints, operator-dependent performance, device-related safety considerations, and lifecycle evidence obligations—that differ from those of pharmaceutical trials. In our structured synthesis, we identified no published framework that explicitly integrates agentic AI into medical device clinical-trial workflows within Taiwan's regulatory context. Drawing on a structured synthesis of 39 core sources (29 scholarly publications and 10 regulatory or standards documents), supplemented by targeted contextual literature on AI governance and implementation, we developed a five-layer framework comprising Regulatory Decision, Evidence Requirement, Data Integration, Agentic AI Optimization, and Adaptive Feedback. The framework links bounded agentic AI functions to TFDA regulatory decision-making, device-specific evidence requirements, device-derived and real-world data, and lifecycle evidence generation. Cross-cutting governance considerations include algorithmic bias and representativeness, transparency and traceability, human oversight and accountability, cybersecurity and privacy, and model-drift and lifecycle change management. The framework is a contextual proposal for Taiwan rather than a validated or implemented system. We provide actionable recommendations for regulators, investigators, AI developers, and infrastructure planners. External assessment and prospective feasibility testing are required before implementation.