Analysis: Three key challenges to scaling AI in health3 Major challenges restricting the expansion of AI in health include lack of political direction and failure to deliver effective regulation. Experts identified three core challenges that hinder the scaling of AI in health: AI lacks priority on policy agendas; technical decisions often misalign with strategic health goals; and a fragmented regulatory landscape undermines public trust. Addressing these issues requires clear alignment between AI initiatives and health objectives, along with transparent, accountable systems to build confidence. Making AI in health strategically and politically relevant requires key challenges to be met. These include the need to show results within a short timeframe in order to maintain support across election cycles and having to deal with budget constraints. The benefits of AI in health are not always immediately clear, making it difficult to access political and financial support due to political pressure to demonstrate results quickly. Policy- makers may hesitate to invest without immediate, tangible benefits. Additionally, the long-term nature of certain AI use cases in healthcare introduces financial uncertainty, which, combined with high upfront costs, can make large-scale AI initiatives less attractive to leaders focused on short-term gains.Lack of understanding of AI’s value in health The value of AI in health remains unclear, leaving stakeholders hesitant and liable to treat AI as a nebulous and potentially dangerous concept rather than a tangible tool. Policy-makers often grapple with understanding the true impact of AI in health because its theoretical benefits often fail to materialize in practice. For instance, technologies that increase patient demand might overwhelm under-resourced health systems, stalling innovation. This is often because of a failure to translate overall goals to AI goals, i.e. to validate, to gain diverse and representative data and to have end-to-end perspectives on how to optimize at different levels.3.1 Complexity of AI in health deterring policy-makers and business leaders The Future of AI-Enabled Health: Leading the Way 15

The Future of AI Enabled Health 2025