In its newly published 2030 strategy, the Medicines and Healthcare products Regulatory Agency (MHRA) has vowed to reduce regulatory barriers for UK start-ups and SMEs, develop a modern regulatory framework for AI as a medical device, and move forward with plans for the use of AI in transforming medicines development.
Introducing the strategy, MHRA chief executive Lawrence Tallon said: “We are in the midst of a period of extraordinary scientific and technological discovery, which is helping to reshape how diseases can be prevented, diagnosed and treated…These ground-breaking developments challenge traditional regulatory models and require innovative approaches to evidence generation, clinical research design, manufacturing oversight and safety and surveillance.”
A strategic priority deliverable under the new strategy is a “real-time, world class safety and surveillance system”, which will harness UK data and AI to better understand medicines and medical device safety in real-world use. Work is ongoing with partners to improve access to linked hospital data and patient genomics for regulatory purposes, according to the MHRA, and analytical capacity will be advanced through recruitment and the use of AI-enabled tools.
Post-market surveillance will be a focus, with a greater emphasis placed on monitoring performance and managing risk once products are in use, the agency states. This includes improvements to the ways medical device data is collected and used, with key systems for registration and reporting to be upgraded, and new pre-market regulations introduced making unique device identifiers compulsory, allowing devices to be traced throughout their lifecycle.
The agency vows to do more to create a “world-class innovation ecosystem”, aiming to identify emerging innovations earlier, and support for proposed legislative reforms to develop statutory mechanisms for regulatory sandboxes. Conditional approval will also be offered for innovative medical technologies that address unmet clinical needs via the Early Access Service for Devices, ahead of full regulatory assessment.
A targeted voucher scheme will be introduced to support small and early-stage companies in accessing “premium regulatory services at lower cost”, in order to reduce regulatory barriers, the MHRA explains, adding: “We will also explore additional ways that the regulatory expertise and knowledge generated in the agency can be utilised on a more commercial basis to support innovation in the life sciences sector and generate value for the UK economy.”
An AI Medicines Development Action Plan is further expected in 2027 to outline a coordinated, UK-wide approach to the use of AI in medicines development. The MHRA expands on this with ambitions to advance its capabilities in real-world data, AI, and computational modelling, publish new guidance on the use of digital twins, enable “hybrid trials” using digital twins and synthetic data, and expand capacity to assess “in-silico” (via computer simulation or modelling) evidence.
To make the framework for regulating AI more adaptive, the agency points to plans to launch expanded Predetermined Change Control Plans, allowing pre-agreed model updates to be implemented without unnecessary delay; and to the potential for master files to be used for foundation models, enabling developers to share essential safety and performance information for AI-enabled medical devices.
On an operational level, the agency also commits to offering a “more predictable and responsive” service, promoting the use of clear timelines and a simpler funding model. This will be supported, it says, by the digital transformation of its systems and expanded data and intelligence capacity, as well as the use of AI “routinely” in automating processes like application triage and evidence pre-screening. An AI-enabled clinical trial protocol checker is reportedly in development, which should enable sponsors to assess their protocols against common grounds for non-acceptance prior to submission.
To accompany the strategy, the MHRA introduces a series of explainers for healthcare professionals, patients, innovators, and the wider medtech industry, outlining the most relevant changes and what those mean in practical terms.
Wider trend: Health AI
The UK government has issued its response to the recommendations made by the National Commission into the Regulation of AI in Healthcare, choosing to accept all 44 findings, and highlight an upcoming implementation plan and roadmap to be published by Spring 2027. The commission published its recommendations for a future regulatory framework in September, finding: “Current approaches were largely designed for products that are more static and easier to reliably assess at a single point in time. AI-enabled products may iterate rapidly, perform differently in different settings and depend on the data, workflows, people and organisations around them.”
In its latest meeting, the NHS England board raised concerns regarding the lack of a single clear vision for the role of AI in the NHS, and the potential for this to lead to delays and duplication. The board moved on to emphasise the importance of developing proportionate and risk-based arrangements “at pace” that can enable safe innovation and continuing assurance throughout the lifecycle of AI systems.
The Public Health Agency of Canada (PHAC) has signed a memorandum of collaboration with Vital, a new collaborative health data platform based out of Unity Health Toronto, focusing on disease surveillance, building capacity with real-time data, and developing AI tools for outbreak forecasting and emergency management. Unity Health Toronto shares that initial areas of focus will be on using hospital data to detect clusters of symptoms for areas such as opioid toxicity and respiratory viruses, building capacity to identify and intervene on public health concerns, and developing AI tools for public health surveillance, emergency management, and the prediction of outbreaks.
