The Future of AI Enabled Health 2025

Through the enquiry, experts listed fundamental challenges that limit the positive impact of AI in health. They categorized them into two groups: 1. Structural constraints governing the operation of AI scaling, which are largely fixed and unchanging. 2. Challenges that can be overcome, for which they believe collaboration between the public and private sectors, e.g. through PPPs, would have the most significant impact. The exercise highlighted five fundamental structural constraints that need to be considered in plotting a successful path to transformation: 1. Political conditions: Election cycles create pressure to show results within two to three years. Additionally, scaling innovation is often limited by national systems.2. Need for scale: Public health outcomes require large-scale and appropriate validation and long- term data to demonstrate significant impact. 3. Resource limits: Budget pressures are universal, with Organisation for Economic Co- operation and Development (OECD) countries spending on average 11% of gross domestic product (GDP) on health, where spending growth exceeds GDP growth. 4. Resistance to change: Inherent resistance to change must be managed, including inertia and conservatism in the medical field. 5. Legacy systems and processes: Existing legacy systems, regulations and incentive models must be addressed to unlock AI’s full potential in health. 1.3 Key barriers The Future of AI-Enabled Health: Leading the Way 11