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These two reports summarise findings from the National Commission into the Regulation of Artificial Intelligence (AI) in Healthcare’s research and engagement activities and call for evidence. The Commission’s purpose is to advise the Medicines and Healthcare products Regulatory Agency (MHRA) on improving its regulatory framework and to accelerate safe access to AI in healthcare and across the NHS. You can read a summary of Patient Safety Learning’s response to this call for evidence here. The work brought together evidence from patients and the public, healthcare professionals, industry, academics and wider health system stakeholders through public polling, surveys, stakeholder engagement, deliberative research, an open call for evidence, a public Ask Me Anything session and insights from the MHRA’s AI Airlock programme. Thorough analysis of this evidence, 10 key findings have been identified. The report summarises these as follows: 1. There is a clear call for a proportionate, lifecycle-based approach to regulation Stakeholders noted that the current framework, which is designed for more static medical devices, is not well suited to iterative and adaptive AI systems. Across groups, stakeholders called for a proportionate approach that is risk-based, considers patients’ safety and fairness, with clear practical guidance and addresses existing duplication and fragmented oversight. Stakeholders also underlined the importance of strengthening clinical evidence requirements, with strong support for enhancing post-market surveillance and improving coordination. With a more proportionate approach seen as essential for balancing innovation with patient safety. 2. There is strong consensus for significant regulatory reform Across respondent groups of healthcare professionals, healthcare providers and industry, most people said that the existing regulatory framework needed “significant reform” but did not need a “complete overhaul”. Amongst patients and the public, the number of respondents calling for “significant reform” and a “complete overhaul” were similar, with 34% asking for “significant reform”, and 35% for a complete overhaul. 3. There was broad consensus that AI systems will increasingly require continuous post-market surveillance and monitoring Several stakeholders highlighted the need to upgrade current approaches to post market surveillance and monitoring, so they are better suited to AI systems. There was strong consensus that performance and risk cannot be adequately assessed through one-off approvals alone but instead require ongoing, real-world oversight across the lifecycle. Through qualitative evidence, stakeholders called for a more continuous and ongoing approach which helps track performance, monitor safety, and manage compliance across the AI system lifecycle. They also suggested that upgraded approaches need to help manage performance drift, validate performance in real world settings, and track changes in performance over time. 4. Responsibility should be shared across the system, with each individual and institution understanding their essential role and responsibilities There was strong consensus that accountability should not rest with a single person or institution, with respondents favouring a model which better distributes liability across the lifecycle. Patients and members of the public called for a comprehensive approach to accountability that addresses current gaps, healthcare professionals stressed that clinical accountability should be maintained whilst healthcare providers emphasised the need for robust governance structures and clear organisational responsibility. Stakeholders also highlighted uncertainty in how roles, responsibilities, and liability are defined and applied in practice. There were differing views on where liability should sit when an AI system causes or contributes to harm. Some respondents believed that liability should sit with the healthcare professional using the AI system. Another group of respondents argued that liability should sit with the healthcare provider who deploys the AI system. Others suggested that liability should sit with manufacturers, given their role in developing the technology and then maintaining their AI system’s performance. Across responses, there was a consistent emphasis on the need for greater clarity and consistency in how liability is allocated. Many respondents called for structured approaches to distributing liability that reflect the roles of different actors, including manufacturers, healthcare providers, and healthcare professionals. Suggested approaches included shared or distributed liability models that apportion responsibility based on specific circumstances. Stakeholders noted that clearer and more consistent frameworks would help address uncertainty and support the safe use of AI systems in healthcare. 5. Human oversight and responsibility for clinical judgment should be retained There was strong consensus from respondents that AI systems should continue to augment the work of professionals and should not be fully responsible for clinical decision making. Patients and the public emphasised the importance of human involvement in their care, including expectations that clinical decisions involving AI should be checked and validated by a human clinician. Healthcare professionals and professional bodies highlighted the risk of over-reliance on AI outputs at the expense of professional judgement. Industry respondents were supportive of ‘human-in-the-loop' safeguards. 6. Transparency and explainability will be key for the ongoing deployment of AI systems The ability to easily understand how an AI system works and to interpret its outputs will be key for building trust, enabling deployment, and ensuring the safety of an AI system. Patients, public and professionals advised that explanations of AI system outputs need to be clear, and providers called for greater transparency in the procurement process for sourcing AI systems. Industry organisations commented on the need for clearer and more structured regulatory documentation. 7. Data access and use is central to the role of AI in healthcare moving forward Respondents to the Call for Evidence noted that healthcare data is simultaneously an enabler and a barrier to the development and deployment of AI systems in healthcare. Patients and public expressed strong concerns about current approaches to consent for data access and how data is used by commercial entities. Some respondents cited governance and compliance burdens and fragmented data infrastructure as key barriers to development and deployment. Industry respondents called for clear and robust frameworks for accessing data including shared data governance templates and clearer guidance on data standards. 8. There is a need for robust training and improved AI literacy The Call for Evidence found a clear view that robust, ongoing training and clear understanding of AI in healthcare is critical for safe adoption. Healthcare professionals highlighted the risks of a lack of AI-specific training can bring such as increased risk of automation bias. Healthcare providers called for more structured workforce training on AI moving forward. Industry respondents advised that training is also needed for individuals who oversee the governance of AI systems in healthcare. 9. There is a need to improve incident reporting and learning mechanisms There were widespread calls for standardised reporting mechanisms for AI systems. Patients and public called for greater transparency and accountability over where AI is involved in care, including clearer communication when things go wrong. Healthcare professionals raised concerns about underreporting of safety incidents in healthcare more broadly, noting that workload pressures are a significant contributing factor. Responses also suggested limited awareness amongst some healthcare professionals that the existing Yellow Card scheme already applies to medical devices, including AI enabled devices. Healthcare providers highlighted the operational challenges of implementing incident reporting consistently across different settings. Industry respondents called for clearer guidance on how incident reporting should work within AI specific post-market surveillance frameworks. Several respondents also proposed improvements to surveillance and monitoring approaches, including establishing a national reporting system for AI incidents and providing guidance for healthcare professionals on what to report. 10. Patient and public engagement, trust, and communication will continue to be key for the deployment of AI systems. Through the Call for Evidence, trust emerged as a core enabler of AI adoption in healthcare. Patients and the public called for consistent involvement, consent, and clarity over the role of AI systems, whilst professionals highlighted the need to take a proportionate approach to explaining how AI is being used to patients. Providers advised that clear and consistent transparency and communication frameworks are needed whilst industry respondents recognised that trust is key for the uptake of AI systems in healthcare.

The Medicines and Healthcare products Regulatory Agency (MHRA) regulates medicines, medical devices and blood components for transfusion in the UK. This roundup provides a summary of their latest safety advice for medicines and medical device users. It includes details of medicine recalls, medical device field safety notices and details of how to report drug reactions and device incidents. This month's Safety Roundup includes: Drug Safety Update on Nasal decongestant sprays and drops containing xylometazoline hydrochloride / oxymetazoline hydrochloride: increased risk of rebound congestion, rhinitis medicamentosa, and tachyphylaxis with overuse. Drug Safety Update on Finasteride and Dutasteride – updated safety warnings for psychiatric side effects and sexual dysfunction. Device safety Information on Kimal Procedure Packs containing recalled components: Namic Angiographic Syringe with the risk of syringe disconnection; Namic Manifolds with the risk of foreign particulates. Important guidance for use in urgent procedures where there are no alternatives. Device safety Information on Risk of severe harm from use of incorrect giving (administration) set for blood transfusion. Device safety Information on Allurion Gastric Balloon: Updated safety information due to the risks of gastric outlet obstruction, small bowel obstruction and gastric perforation. Letters, medicines recalls and device notifications sent to healthcare professionals in May 2026. News and guidance on: Dostarlimab (Jemperli) and immune-related skin adverse reactions: updates to the product information. BNF and BNFC updated guidance on medicines that cause drowsiness to help prevent co-sleeping deaths.

The use of artificial intelligence (AI) continues to increase across health and social care. As England’s independent regulator of health and social care, the Care Quality Commission (CQC) encourages the use of innovative technologies, including AI, where the technology benefits people and results in more effective and efficient services. AI presents enormous opportunities, though not without risks. The CQC have outlined some of the benefits and risks of AI and set out how CQC’s regulatory work has a role in ensuring AI contributes to safe, equitable and person-centred care. The principles of good use of AI provide a high-level illustration of what this means for providers of health and social care services.

The letter attached is from Professor Catherine Ross, Chief Scientific Officer for Scottish Government, with an update on the work relating to ‘Healthcare Science in Scotland’ and the theme of ‘quality, safety and, assurance’ as set out in Healthcare Science in Scotland: Defining Our Strategic Approach.    The Scottish Government has commissioned the Professional Standards Authority (PSA) to carry out a Right Touch Assurance (RTA) assessment of the Healthcare Science workforce. This work will examine the potential risk of harm to patients, particularly as many roles in Healthcare Science are not currently subject to statutory regulation. The assessment aims to inform future decisions about whether additional regulation may be needed to strengthen quality, safety and assurance across the profession. Stakeholder engagement will form a key part of the process, with opportunities to contribute evidence and views. A final report is expected by Spring 2027, after which the Scottish Government will consider next steps.

This webinar, hosted by the International Association of Medical Regulatory Authorities (IAMRA), explored how medical and health profession regulators and broader patient safety systems can work more closely together to strengthen care and reduce harm to patients. It aims to help improve understanding of the opportunities to better connect people and system focused safety systems, including through potential partnership models, to best support and ensure a safe and competent health workforce. Facilitator: Professor Martin Fletcher, IAMRA Board Member Speakers: Helen Hughes, Chief Executive, Patient Safety Learning, UK, and Dr Gerry Hickson, Vanderbilt University Medical Center, USA. Related reading on the hub: Professional regulation and patient safety systems: parallel planets or partners in improvement? Professional regulation and patient safety: parallel systems or purposeful partners?

Surgical implants, such as joint replacements, are used for many serious conditions. Innovation continues to supply new implants, including outputs of the soft robotics revolution. However, they carry risk of complications with potentially devastating consequences. This opinion paper provides the reflections of two surgical technologists on present challenges to safety, efficacy and broad implementation of medical implants. They highlight lack of familiarity with implant surgery in healthcare services, with concomitant risk. First-in-human application of new implants is not sufficiently standardised and regulated. IDEAL-D is a structured framework for medical devices (Idea, Development, Exploration, Assessment, Long-term study). Once CE-marked and approved for mainstream use, there are problems with the implementation. ‘Early adopter’ surgeons and centres face cultural inertia, lack of funding support and issues around training, especially learning curves. Patient selection may not be well-defined, and complications inaccurately reported, affecting implant dissemination detrimentally. The Cumberlege report showed how harmful this can be. There is need to standardise early clinical studies. Implementation of implantable devices requires changes to whole-team training, funding and post-implementation reporting. The IDEAL-D framework represents an important step, but other system-wide changes are required if implants are to achieve their intended clinical impact.