A government-appointed commission has recommended a substantial redesign of UK healthcare AI regulation, calling for continuous lifecycle oversight, clearer rules for systems that change after deployment, stronger post-market monitoring, and greater transparency around technology dependencies.
The National Commission into the Regulation of AI in Healthcare has published 44 recommendations intended to shape a future framework for software and AI-enabled medical devices, with a cross-government response due separately.
Its central conclusion is that a regulatory system designed largely around comparatively static medical devices is increasingly ill-suited to software that can change over time, perform differently in different clinical environments, or depend on data, workflows, people, and other technologies around it.
The Commission has recommended that the Medicines and Healthcare products Regulatory Agency review and update the UK Medical Devices Regulations where necessary, including clearer definitions of when software and AI products should be regulated as medical devices.
Evidence requirements would also become more lifecycle-based. Rather than concentrating assurance predominantly before a product reaches the market, regulators would place greater emphasis on monitoring performance after deployment, with requirements tailored to the risk, purpose, and technical characteristics of individual systems.
The report recommends greater use of real-world evidence, staged routes to market, regulatory sandboxes, and stronger post-market arrangements to provide continuing assurance as systems are updated or used in different settings.
Adaptive AI is a particular regulatory challenge because changes can be introduced after deployment. Conventional approval based around one fixed version becomes harder to apply where a model is updated frequently or its performance is sensitive to the clinical environment in which it operates.
More flexible routes for safe iteration could reduce repeated approval work for developers, but would place greater emphasis on monitoring, evidence generation, and clear limits around acceptable changes.
The report extends beyond product authorisation. It calls for clearer responsibility across healthcare organisations, improved AI governance, stronger workforce capability, more accessible guidance for developers, and greater coordination between the MHRA and other regulators and assurance bodies.
The Commission also recommends clearer, earlier, and more predictable engagement between developers and regulators. One proposal is a formal classification-confirmation service allowing manufacturers to obtain a written determination on whether and how a product falls within medical-device regulation.
That could reduce costly uncertainty during development, particularly for smaller health-technology companies deciding which evidence programme, clinical studies, or quality systems will be required before reaching the market.
Healthcare providers face a related challenge after procurement. Adopting AI increasingly involves assessing how products will be monitored, updated, integrated into clinical workflows, governed, and ultimately withdrawn, rather than judging performance only at the point of purchase.
These requirements become more important as health systems adopt technologies ranging from diagnostic software to generative and agentic AI. Potential benefits include earlier diagnosis, automation of administrative work, and more personalised care, but operational risk increases as systems become more autonomous and more closely integrated with clinical decisions.
The regulatory balance is consequently difficult. Excessively rigid rules can slow useful innovation and make routine software updates burdensome, while weak oversight can transfer risk into live clinical environments without sufficient monitoring.
The Commission’s model attempts to bridge that gap through proportionate regulation across the full product lifecycle, with the level of scrutiny linked to risk and benefit rather than treating every software change in the same way.
Implementation will now depend on decisions by government, the MHRA, healthcare bodies, and other regulators. Some recommendations may be achievable through guidance and operational changes, while others could require amendments to existing regulation.
The separate government response will determine which proposals are taken forward. The direction of travel is nevertheless towards continuing evidence and assurance after deployment, placing greater responsibility on developers and healthcare organisations throughout the working life of an AI system.
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