Artificial intelligence and evidence

Artificial intelligence (AI) is increasingly shaping evidence-informed policy-making (EIP) in health by enabling faster analysis, synthesis and use of large and diverse data sources across the policy cycle. This discussion paper examines the intersection of AI and EIP, outlining how AI can support problem identification, policy design and implementation through enhanced data integration, predictive modelling, scenario simulation and adaptive feedback. It emphasizes that AI augments rather than replaces human judgement, while highlighting its potential to expand the evidence base and support more timely, responsive and iterative decision-making in complex health contexts.

The paper analyzes key risks and challenges associated with AI integration, including bias, opacity, equity concerns, data governance and regulatory gaps, and considers how these may affect different stages of the policy cycle. It reviews relevant governance frameworks and identifies areas of alignment between EIP and AI governance traditions. It further proposes practical considerations for responsible implementation, including human oversight, multidisciplinary collaboration, living evidence approaches and risk-based regulation. Intended for policy-makers, regulators and health stakeholders, the document provides a structured basis for leveraging AI to strengthen policy processes, improve health outcomes and maintain public trust.