The UK's medicines regulator has called for new laws to govern AI products used in the NHS and other healthcare settings, publishing 44 recommendations as part of a broader policy overhaul. The Medicines and Healthcare Products Regulatory Agency (MHRA) says current rules were designed for physical devices like hip replacements and stethoscopes, not software that learns and changes after approval.
MHRA chief Lawrence Tallon told the BBC that AI will soon be a routine part of NHS care. "What I would expect is that patients will... increasingly see AI as part of the way that normal NHS healthcare is delivered," he said. "That should happen in a way that they can maintain their trust and their confidence in what's happening."
The report, compiled by an independent commission with input from more than 12,000 patients and clinicians, outlines a regulatory framework for a technology that behaves differently from anything the agency has overseen before. "Unlike most of the medical products we're used to regulating, these products continue to change after the point of authorization," Tallon said. "As new data gets fed in, they learn, they adapt, they drift."
What the recommendations include
The commission's proposals cover monitoring, transparency, and accountability. Key recommendations include:
- Continuously monitoring AI products and removing them from regulatory approval if they malfunction or become less effective over time
- Giving patients the right to know whether AI is involved in their care, with easy access to information about the products used
- Granting regulators the power to penalise developers if an AI product fails to meet required standards
- Creating an AI "L plate" system that allows new models to be trialled by healthcare professionals under close supervision
Tallon acknowledged that regulating AI is a global challenge. "I don't think at this moment in time we can point to a single country, a single regulatory framework, and say that they have absolutely cracked it," he said.
How AI is already being used in UK healthcare
AI note-takers known as scribes, powered by large language models, are reportedly used by 40% of UK-based GPs to record consultations and generate reports. But adoption is not frictionless. A recent University of Edinburgh study found patients could be less likely to share personal information such as substance abuse history if they knew the conversation was being processed by AI.
Professor Henrietta Hughes, a GP who served on the report commission, said many of her patients were comfortable with AI during consultations, but some chose to opt out. "Some say, 'I don't want to talk to a robot', and that is also fine," she said. Hughes acknowledged that scribes can make mistakes in their notes, but said doctors are responsible for correcting them.
Professor Alastair Denniston, an ophthalmologist who also worked on the report, described the technology as "an exceptional opportunity" for healthcare, "likely to rank alongside step-changes such as antibiotics and MRI". For professionals working in AI for Healthcare, the regulatory direction set by the MHRA will shape how these tools are deployed and evaluated in clinical settings.
The regulatory gap
The MHRA's current medical device framework predates the era of adaptive algorithms. Tallon explained that while existing guidelines may work for simple AI products trained to spot known symptoms on scans, they do not cover more complex models that evolve after deployment. The commission's recommendations aim to close that gap without stifling innovation.
For AI for Regulatory Affairs Specialists, the report signals a shift toward ongoing oversight rather than one-time approval. Products would be monitored continuously, with mechanisms to withdraw authorisation if performance degrades - a departure from the static certification model applied to traditional medical devices.
Why this matters for healthcare professionals
If the MHRA adopts these recommendations, clinicians will need to understand not just how AI tools work, but how they are regulated, monitored, and held accountable. The proposed "L plate" system would put new AI models into clinical environments under supervision, meaning frontline staff may be asked to evaluate tools that are still learning. Patient transparency requirements would also change how clinicians communicate about AI involvement in care decisions. Healthcare professionals who can navigate both the clinical and regulatory dimensions of AI will be better positioned as these rules take shape.
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