AI applications are increasing in scope, spanning clinical decision support, diagnostics, public health system management, as well as administrative and operational functions such as medical scribes, and other areas. Given the rapid deployment of AI and its demonstrated advantages for specific tasks, the question is no longer “should we adopt AI in health?”, but “how do we adopt AI effectively and safely?”

Africa is producing some of the world’s most contextually grounded health innovations. AI-powered diagnostic tools, drone-enabled last-mile delivery, and mobile platforms are already reaching communities that formal health systems have long underserved. Yet most of these tools remain trapped within national borders.

Commissioned as the research partner to the MHRA's independent National Commission into the Regulation of AI in Healthcare, this report brings together findings from an annual polling of public attitudes towards AI with new findings from a UK-wide public deliberative exercise exploring what the public thinks regulators should prioritise and what is needed to build and maintain public trust in AI regulation.

As AI use grows in clinical practice, the law has not kept pace. "Closing the AI Liability Gap: AI, safety, and the case for legislative change" highlights gaps in UK product liability law. In particular, when using AI recommender systems there is potential for clinicians to be unfairly exposed to clinical negligence claims in instances when AI outputs go wrong.

Efforts to research, implement and scale responsible AI for addressing health challenges in LMICs are often fragmented. It limits collaboration and slows progress. Communities of Practice offer a promising approach to fostering shared learning and supporting the use of AI to strengthen health systems and improve health outcomes.

Media-reported AI incidents and hazards offer valuable insights into the types, frequency, and impact of AI-related harms. This paper analyses real-time data from the OECD.AI Incidents and Hazards Monitor (AIM) and tracks how attention shifts across themes such as synthetic media, child safety, cyberattacks, etc.

This report includes a policy checklist to guide decision-making and prioritization in creating the enabling environment to scale AI’s potential in transforming healthcare systems, organized into four pillars: i) establishing enablers; ii) implementing guardrails; iii) engaging meaningfully with the public, providers and industry; and iv) deploying trustworthy AI.

This document refers to the deveopment and deployment of general-purpose AI systems in a wide range of contexts that may impact public health, well-being, and healthcare, without prejudice to further regulatory requirements for clinical settings and medical devices.