Comment


AI: fear, facts and fantasy EDITOR’S COMMENTwith LOUISE FRAMPTON


THE CLINICAL SERVICES JOURNAL Editor


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It is fair to say that AI is receiving a heavy backlash. Evan Hubinger, from Anthropic, made international headlines by sharing his belief that there is a greater than 10% chance AI “could kill all humans” within the next decade; Jack Clark, a founder of Anthropic, said that an AI “kill switch” may need to be mandatory; while comparisons to ‘Terminator: Judgement Day’ have made sensationalist reading on social media. In rather less headline-grabbing news, the MHRA announced its recommendations, in the same month, on how the UK can strengthen the regulation of AI in healthcare, so it remains “safe, keeps pace with innovation, and continues to earn people’s trust.” AI is already used in the NHS to spot serious health problems early, for example strokes and skin cancers, and to free up clinicians’ time through voice-enabled technologies. In fact, AI is predicted, by some, to become “as common in healthcare as the stethoscope.” (https://tinyurl.com/mvcvysn2) It is widely understood that this fast pace of development needs to be matched with effective


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regulation that doesn’t inadvertently hamper innovation. There is a need for robust safeguards that protect patients from risk and inequalities, while adoption must be “patient centred” to ensure that “no one is left behind”. However, the BBC recently reported that patients in Rotherham have struggled with a new AI GP receptionist because it could not understand their accent. (https:// tinyurl.com/2r4wspnh) We also know that AI can inadvertently automate and perpetuate human biases and societal inequities. (https://tinyurl.com/jp9rpcb7) Furthermore, there needs to be recognition that AI software cannot capture the more subtle nuances of patients’ accounts – the unspoken subtext, intonation, and non-verbal cues that only human clinicians can give meaning to. (https://tinyurl.com/86x85nm3) On top of this, there are the known risks associated with ‘drift’ and ‘hallucinations’. These are just some of the reasons why an independent Commission was established by the MHRA in September 2025. The Commission has recommended that the MHRA introduces staged authorisations for new AI models, similar to ‘L-plates’ for learner drivers. New models would be deployed under close supervision and tight guardrails, allowing them to demonstrate real-world safety and performance before being granted fuller authorisation. This staged approach is designed to give UK patients world-first access to promising new models, while ensuring risk is carefully controlled at every step. Rather than a single, point-in-time approval, the Commission recommends that AI-enabled medical devices should be subject to continuous, real-world monitoring throughout their working life. This reflects how AI technologies evolve after they are deployed, rather than at the point of approval alone. This would help ensure that as AI systems learn and adapt, patients remain protected and regulators have the visibility they need to act quickly if problems emerge. The Commission also recommends that members of the public should be able to easily search for information about the safety of specific AI-enabled medical devices, including any adverse incidents. This would build on tools such as the MHRA’s interactive Drug Analysis Profiles, giving patients and the public clear, accessible information they can trust. Alongside this, patients have told the Commission that they want to know when AI is being used in their care. Professor Neil Lawrence, DeepMind Professor of Machine Learning, University of Cambridge, summed up the challenges with the following comment: “Generative AI is not like other medical technologies we’ve regulated before. It can behave differently in different circumstances, and it can evolve after its initial deployment. That means we can’t rely on a single point of approval and assume the job is done.” Most importantly, he highlighted the need for honesty about the uncertainties in these systems, so that “patients and clinicians can judge when their outputs are applicable, and when those outputs should be questioned.” Ultimately, AI has enormous potential to bring transformative benefits to the NHS, but patient and public engagement, trust, and communication will be key for its successful deployment going forward. These are exciting, yet uncertain times…


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