National Commission into the Regulation of AI in Healthcare: research, engagement and call for evidence findings (MHRA, 11 June 2026)

Summary

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.

Content

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.