Regulatory authorities in the US, EU, and UK are converging on structured pathways for medical technologies that use adaptive artificial intelligence, aiming to keep innovation aligned with safety and performance obligations. Typical regulatory routes still follow established device classifications and conformity assessment mechanisms, but sponsors are expected to explain how learning systems behave over time, how model updates are controlled, and how clinical performance is assured as software evolves. This places particular emphasis on predictable change processes, documentation of training and validation data, and alignment between declared intended use and real world behavior.
Post market surveillance expectations are being expanded for products using adaptive artificial intelligence, reflecting regulators’ concern that real time learning and frequent updates can shift performance after initial approval. Manufacturers are expected to implement continuous monitoring frameworks, with clearly defined metrics for safety, effectiveness, and data quality, and to collect and analyze field performance data in a structured way. Feedback from users, incident reports, and real world evidence need to feed into a formal surveillance plan that can trigger corrective and preventive actions when performance drifts, and that supports regular reporting obligations to competent authorities.
Adaptive artificial intelligence and change control are increasingly organized under concepts such as predetermined change control plans, which define in advance what kinds of model or software changes are allowed without a new full regulatory submission. Required elements typically include clear change boundaries, predefined validation methods, and risk management approaches that address both functional and cybersecurity impacts of updates. Cybersecurity is treated as a core safety element, with expectations that manufacturers design secure architectures, maintain vulnerability management processes, and ensure that any remote or automated updates to artificial intelligence systems are authenticated, traceable, and resilient against malicious interference.
