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.

This extensive report points out a range of risks, including health-related risks, from the unsafe development and deployment of general-purpose AI systems. It delves into risk mitigation strategies to inform the development, deployment, and governance of general-purpose AI systems.

This document sets guidance to assist Member States in mapping the benefits and challenges associated with use of LMMs for health and in developing policies and practices for appropriate development, provision and use.

This document presents ten guiding principles for the development of medical devices with machine learning.

The document establishes two new mechanisms within the UN system to promote international cooperation on the governance of AI.

This document presents an overview of AI governance across regions, touching upon risks, applications, and governance challenges for health and other areas.