Neil Lawrence's Talks
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Why AI Needs Proportionate Lifecycle Regulation

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at National Commission into the Regulation of AI in Healthcare — Recommendations Launch Event, MHRA on Sep 10, 2026 [reveal]
Neil D. Lawrence, University of Cambridge

Abstract

AI is already in healthcare. The question the National Commission heard repeatedly was not whether it should be used, but how it can be used safely, fairly and transparently — with clear benefit, meaningful human oversight and continued public trust.

As Chair of the Technology Working Group, Neil Lawrence sets out why current regulatory approaches — largely designed for more static technologies — are being outpaced by software and AI-enabled products that evolve, drift and depend on the settings in which they are deployed. He reflects on what the Commission learned about gaps and overlaps across the regulatory landscape, and why the first pillar of the report — proportionate lifecycle regulation — is the foundation for a framework that is safe, fast and trusted.

The Question We Heard

National AI Commission — commissioner framing

The National Commission into the Regulation of AI in Healthcare was established by the MHRA in September 2025 as an independent expert advisory body. Chaired by Professor Alastair Denniston and Deputy Chair Professor Henrietta Hughes, it brought together expertise from healthcare, technology, law, patient groups, government and the NHS across all four nations. Four specialist working groups — Cross-Whitehall, Devolved Authorities, Health Systems and Technology — helped ensure recommendations were grounded in how technologies are developed, deployed and governed in practice.

What came through consistently in the Commission’s Research and Engagement programme was not a debate about whether AI should have a role. The question was how to ensure these technologies improve care in ways people can trust. Support for innovation is strong, but it is not unconditional.

What came through consistently was not a debate about whether AI should have a role. For many people, that future is already here. The question was different: how do we ensure these technologies improve care in ways people can trust? Support for innovation is strong, but it is not unconditional. People want reassurance that new technologies are introduced responsibly, that accountability remains clear, and that benefits are felt by patients and the public — not only by developers and institutions.

Designed for a Different Era

Software and AI-enabled medical devices present regulatory challenges that differ from those associated with traditional medical devices. Unlike many conventional devices, AI products may evolve over time. Their performance is context-dependent and sensitive to the data, workflows, people and organisations around them.

The current regulatory framework for medical devices was not designed for technologies whose design and performance can change across a lifecycle. It generally relies on performance and safety data at the pre-deployment stage, with insight on adverse outcomes gathered reactively after market. That balance is heavily weighted towards pre-market assurance. It does not work well when performance in the clinic diverges from performance in validation, or when models are updated frequently to remain safe and effective.

The Commission’s central conclusion in this area is that regulation and assurance must become more proportionate, lifecycle-based and system-wide. Not heavier everywhere — smarter across the whole journey from development through deployment, monitoring, updating and learning from real-world use.

What the Technology Working Group Saw

The Technology Working Group was asked to provide technical insight on AI capabilities and their regulatory implications — to ground recommendations in how technologies are actually developed and deployed. Alongside the Health Systems, Cross-Whitehall and Devolved Authorities groups, we tested whether the report’s directions were workable in practice.

What struck me most was twofold. First, a genuine willingness to collaborate. Stakeholders across the ecosystem wanted a framework that protects patients and enables beneficial innovation to reach them. Second, a recurring difficulty in knowing who is in charge. AI in healthcare sits across product regulation, data protection, professional standards, organisational governance, consumer protection and more. Depending on intended purpose, functionality and context of use, different regimes apply — or appear to apply.

In the Working Group we kept returning to gaps and overlaps analysis. Gaps where accountability thins and patients have no clear route when something goes wrong. Overlaps where multiple bodies regulate similar ground, imposing duplicated burden on innovators and confusion on providers. Neither gap nor overlap serves safety or trust. The report’s first chapter is not a call for more regulation everywhere. It is a call for right-sized regulation across the lifecycle — coordinated where regimes meet.

Proportionate Lifecycle Regulation

Chapter 1 sets out recommendations for a more proportionate, risk-based and lifecycle-focused approach — from understanding when a product is a regulated medical device through pre-market evidence, efficient routes to market, post-market monitoring and action when something goes wrong.

Several themes matter for today’s session. Qualification and classification need to be clearer for software and AI-enabled products, including low-risk clinical decision support and administrative tools where medical device regulation may not add value. The MHRA’s AI Airlock programme has already shown how intended purpose must be defined and maintained when products evolve — that learning feeds directly into the report.

Function-based regulation recognises that a single product may combine regulated and unregulated capabilities. Oversight should focus on the medical-device function, coordinated with other sector regulators so we reduce overlap rather than create it. Lifecycle-based evidence rebalances pre-market and post-market requirements: when real-world performance is the best guide to safety, regulation should say so explicitly — through predetermined change control, staged authorisations and tailored post-market surveillance.

The aim is a framework where the MHRA can focus scarce expert resource on products that benefit from closer scrutiny, while low-burden pathways and clearer guidance help beneficial technologies reach patients sooner — without lowering the bar where risk warrants it.

Safe, Fast and Trusted

The Commission’s vision is for a regulatory and assurance framework that is safe, fast and trusted. These are not rivals. A framework that is opaque and unpredictable slows innovation; one that is permissive without learning from deployment erodes trust. Proportionate lifecycle regulation is how we hold all three together.

For patients and the public, that means greater confidence that AI used in care is safe, effective and properly governed — with transparency about when and how it is used. For developers and manufacturers, it means clearer expectations and more predictable pathways through development, deployment and improvement. Regulation should not operate as a barrier, but as a framework that helps innovators, regulators and healthcare systems work together. For regulators and government, it means a more joined-up system that learns from real-world evidence, responds to emerging risks and adapts as technologies evolve.

Handover

The recommendations in Chapter 1 are the Commission’s view of what must change in product regulation. Realising them depends on the MHRA, government and system partners — and on the kind of collaboration we saw throughout the Commission’s work. I am delighted to hand to colleagues who are already turning proportionate lifecycle regulation from principle into practice.