UK Advisory Panel Calls for Continuous Monitoring and New Laws Governing AI Medical Devices

The UK is being urged to rewrite its framework for AI-enabled medical devices before such tools become routine across the NHS. The Medicines and Healthcare products Regulatory Agency has published recommendations calling for stronger oversight of software that can change after deployment, including systems used for diagnosis, monitoring and treatment decisions. The push follows Britain’s departure from the European Union, which left the country operating a domestic medical-device regime rooted in older EU rules.

AI creates a regulatory problem because a device’s behavior can shift as data, software or models change. Existing approval processes were designed around relatively fixed products, while machine-learning systems can require ongoing evaluation. The MHRA’s Software and AI as a Medical Device Change Programme sets out a separate reform track alongside the broader consultation on medical-device regulation.

The central recommendation is a staged approval process tied to continuous monitoring after a product reaches patients. The approach would allow regulators to assess performance over time, rather than treating market authorization as the final safety checkpoint. The advisory panel’s recommendations also call for clearer rules covering software updates, changing algorithms and evidence requirements for products that learn from real-world use.

That model would give the MHRA more information about how devices perform in NHS settings, where patient populations, clinical workflows and available data differ from controlled trials. It would also create defined points for reassessment when a manufacturer changes a model or expands its intended use. The proposal aligns with the regulator’s stated aim of making requirements proportionate while protecting patients and supporting responsible innovation.

The reforms are driven by the consequences of errors in healthcare. An inaccurate recommendation from an AI medical device can affect diagnosis, triage or treatment, and clinicians need enough information to understand when a system is reliable and when it should be overridden. New rules would need to address validation, transparency, bias, cybersecurity, data governance and responsibility when performance deteriorates.

The NHS will also need operational safeguards beyond product certification. Healthcare organizations must know which systems are approved, how updates are recorded and who investigates adverse events. Guidance from UK health and research bodies has emphasized that AI oversight spans development, clinical research, procurement and deployment, creating a coordination challenge for the MHRA and other public institutions. The government is gathering evidence for a National Commission into the Regulation of AI in Healthcare, which adds a broader policy layer to the device-specific reforms.

Britain’s regulatory overhaul is also a market-access project. The current Medical Devices Regulations 2002 preserve elements of the former EU system, including classification approaches that rely on listed device categories in some areas. Legal advisers say the UK is considering a more risk-proportionate framework, with reforms affecting software, medical devices and in vitro diagnostics.

Manufacturers will have to manage the relationship between UK requirements and rules in the European Union, including the EU Medical Device Regulation and EU Artificial Intelligence Act. Divergence could let the UK move faster in selected areas, but it could also increase testing, documentation and certification costs for companies selling on both sides of the Channel. The MHRA’s program includes work on clearer software classifications and a regulatory sandbox designed to let innovators test approaches with officials before full market entry.

AI developers will need evidence that covers more than initial model accuracy. They will have to document training data, intended users, clinical context, update procedures and post-market performance. Continuous monitoring also shifts part of the compliance burden into the operating phase, requiring systems for incident reporting, version control and detecting performance drift.

Hospitals will face their own governance demands. Procurement teams will need to evaluate how a device was validated, how it handles local populations and what happens when an update changes clinical behavior. A clearer approval route could reduce uncertainty for responsible developers, while poorly coordinated rules could slow adoption through duplicate reviews. The MHRA has said its reforms are intended to make regulation clearer and proportionate as software becomes more central to medical care.

The UK’s immediate task is to turn broad recommendations into enforceable rules, technical guidance and institutional responsibilities. The MHRA’s consultation and software change programme provide the framework, while evidence-gathering on AI in healthcare is intended to inform wider recommendations from the National Commission.

The timing matters because AI tools are moving from pilot projects toward routine NHS use. The FDA is also developing guidance for AI and software as medical devices, while European regulators are applying new device and AI rules. The UK will therefore be judged on whether it can protect patients, monitor adaptive systems and give manufacturers a predictable route to market without creating a second layer of bureaucracy.