UK Watchdog Demands New Laws for AI in Healthcare as Public Support Hinges on Transparency

MHRA Calls for Comprehensive Overhaul of AI Regulation in Healthcare

The Medicines and Healthcare Products Regulatory Agency has published 44 recommendations demanding new laws for AI in healthcare, acknowledging that Britain's current regulatory framework is ill-equipped to handle the rapid deployment of AI products used in healthcare settings1

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. The UK regulation watchdog, which oversees all medical devices and licenses treatment drugs, compiled the report through an independent commission that gathered input from more than 12,000 people including patients, clinicians, and technology experts1

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MHRA chief Lawrence Tallon emphasized that AI-enabled healthcare will soon become routine within the NHS, stating that patients will increasingly see AI in healthcare as part of normal NHS healthcare delivery1

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. The challenge lies in maintaining patient trust while integrating these evolving technologies. Current guidelines may work for simple AI products trained to spot known symptoms on scans, but they fall short when addressing more complex models that continue to change after authorization1

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Key Recommendations Target Lifecycle Management and Patient Rights

The MHRA recommendations center on three connected areas: proportionate lifecycle regulation, system-wide responsibility, and trust through transparency2

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. Specific measures include continuous monitoring of AI-enabled devices with the power to remove them from regulatory approval if they malfunction or become less effective over time1

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. The framework also grants patient rights to know whether AI is involved in their care and provides easy access to information about the products being used1

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Regulating AI in the healthcare sector presents unique challenges because these products continue to learn, adapt, and drift as new data gets fed in—unlike traditional medical devices like hip replacements, knee replacements, stethoscopes, or plasters that remain static after approval1

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. The proposed framework includes an AI "L plate" system that would allow new AI models to be trialled by healthcare professionals under close supervision, alongside the power to penalize developers if products fail to meet required standards1

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Public Support for AI in Healthcare Comes With Conditions

Public confidence emerged as essential to realizing the benefits of AI in healthcare, with research showing that public support for AI in healthcare is not unconditional2

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. People are demanding evidence of benefits, accountability, and meaningful human oversight before fully embracing AI-enabled healthcare2

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. The National Commission, chaired by Professor Alastair Denniston and Deputy Chair Professor Henrietta Hughes, found that people are open to AI improving their care, but only if it is safe, overseen by humans, and if they know when it is being used2

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Current AI deployment in the NHS includes AI note-takers known as scribes, which are reportedly used by 40% of UK-based GPs to record consultations and generate reports1

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. However, 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 AI1

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. Professor Hughes acknowledged that while many patients are happy with AI being used during consultations, some choose to opt out, saying "I don't want to talk to a robot"1

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Global Challenge Requires Coordinated Approach to Responsible AI Deployment

Tallon acknowledged that regulating AI in the healthcare sector represents a global challenge, noting that no single country has yet "absolutely cracked it" when it comes to establishing a comprehensive regulatory framework1

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. The UK government's Ten Year Health Plan for England and the Life Sciences Sector Plan outline ambitious goals of delivering one of the most AI-enabled healthcare systems in the world, with AI used across a wide range of applications from administrative tools to decision support for healthcare professionals and direct-to-consumer products2

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Responsible AI deployment in healthcare requires collaboration across manufacturers, healthcare providers, healthcare professionals, regulators, and policymakers2

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. While there is excitement about AI's potential in medical research—with Professor Denniston describing it as "an exceptional opportunity" likely to rank alongside step-changes such as antibiotics and MRI1

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—concerns remain about AI models making wrong decisions due to biases in patient data used to train them and incorrect medical advice being given by AI chatbots1

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. The framework aims to support safe innovation throughout the product lifecycle while protecting patients and providing confidence for healthcare providers through proportionate oversight and data sharing mechanisms2

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