Earning Trust for AI in Health 2025

Executive summary AI will reshape healthcare, but realizing its full potential requires responsible governance, trust and global collaboration. Healthcare systems globally face growing pressures: rising costs, workforce shortages and persistent inefficiencies. In this context, AI offers transformative opportunities to enhance patient outcomes and optimize system performance. However, realizing AI’s benefits in healthcare demands responsible development, rigorous evaluation and a deliberate focus on building trust among stakeholders. Today’s medicine regulatory frameworks – largely designed for pharmaceuticals and medical devices – are not fully suited to manage the probabilistic, dynamic nature of AI technologies. Traditional evaluation methods, which emphasize pre-market validation, struggle to accommodate AI systems that evolve post-deployment. As AI adoption accelerates, regulatory models must evolve accordingly. This paper, developed through a collaboration between the World Economic Forum’s Centre for Health and Healthcare and Boston Consulting Group (BCG), identifies three urgent priorities to earn trust for AI in health: 1. Address fragmentation and build technical capacity –Current AI ecosystems are fragmented, and many health leaders lack a deep understanding of AI technologies. –Health systems must build technical literacy among decision-makers to critically assess and responsibly integrate AI solutions. 2. Adapt evaluation and regulatory frameworks –New approaches, such as regulatory sandboxes, post-market surveillance and life-cycle monitoring, are essential. –Guidelines must complement legislation to enable innovation while maintaining high standards of safety, effectiveness and equity. –Independent quality assurance resources and real-world testing environments, such as those being developed under initiatives like the Testing and Experimentation Facility for Health AI and Robotics (TEF-Health), can support more dynamic development. 3. Promote public–private collaboration –Public–private partnerships (PPPs) should move beyond consultation to active co- development of evaluation standards and monitoring frameworks. –Such collaboration is vital to ensure that regulatory practices keep pace with AI innovation while safeguarding patient trust and public health objectives. This paper also emphasizes the importance of global coordination. Divergences in AI regulatory approaches across regions – especially between the Global North and Global South – risk creating barriers to the scalable deployment of AI in healthcare. Capacity-building efforts, especially in under-resourced health systems, are crucial to ensure equitable benefits from AI advances. Ultimately, the future of AI in healthcare must be grounded in adaptability, transparency and shared responsibility. By strengthening evaluation processes, building technical capacity and fostering structured public–private collaboration, health systems can unlock the transformative potential of AI while upholding patient safety and trust and ensuring broader access to innovation. The path forward demands continuous innovation not only in technology but also in regulation and system design. The time to act is now, to ensure that AI fulfils its promise of delivering better health outcomes for all. Earning Trust for AI in Health: A Collaborative Path Forward 4