Countries around the world are taking diverse approaches to implementing artificial intelligence (AI) in health and care systems, guided by the need for clear strategy, regulation, and collaboration. Norway has developed a Joint Action Plan to ensure the safe and effective integration of AI within its health services. Its strategy focuses on cross-agency cooperation and regulatory clarity, underpinned by a strong quality assurance framework to evaluate and compare AI systems. In England, the health service is preparing an AI Strategic Roadmap for the NHS and adult social care, which will act as a practical and evolving guide for future priorities. Catalonia’s Health/AI Programme is built around the principle of “doing more and better with fewer resources,” emphasizing enabling development through access to data, usage rights, and rigorous testing rather than direct ownership of AI tools. It has also introduced a Health Technology Assessment Framework tailored specifically for AI. Meanwhile, Estonia’s more flexible regulatory environment has created space for innovation, with AI seen as a vital tool to offset workforce shortages caused by a declining population, encouraging startups to focus on automation and efficiency.
Building public and professional trust has emerged as a central factor in successful AI implementation. Estonia has maintained a high-trust environment by consistently demonstrating the value of digital services, showing citizens tangible benefits from their data use. Catalonia has launched an AI observatory to register and monitor all health algorithms, ensuring citizens’ right to know when AI is used in their care. Transparency and accountability are seen as essential for public confidence. England’s approach recognizes that technology adoption must go hand-in-hand with cultural change and workforce development. Through initiatives like the AI Ambassadors Network, which includes over 10,000 members, the NHS is promoting AI literacy, digital skills, and public engagement. In Catalonia, strong clinical leadership has been crucial for driving both technical and cultural change, ensuring clinicians play a central role in evaluation, adoption, and capacity building.
A phased and iterative implementation approach is another common thread among these countries. Norway began with low-risk, high-impact applications such as administrative optimization before expanding into clinical use cases like radiology image recognition. This gradual progression allows for learning and reduces risk. Estonia’s experience, having built its digital infrastructure from the ground up, highlights the advantages of starting small, making incremental investments, and verifying results to inform future regulations and deployment. The country’s digital maturity and interoperability now position it as a model for others. In England, success depends on rebuilding the NHS’s digital and data foundations to support scalable AI integration across the entire patient journey, rather than isolated applications. Estonia is also advancing by establishing an AI-enabled services layer for government, designed to connect various systems and enable rapid service development.
Collectively, these experiences demonstrate that successful AI integration in health and care relies on strategic direction, transparency, trust-building, and solid digital foundations. Clear regulation, participatory governance, and an iterative learning process are proving vital to ensuring that AI enhances, rather than disrupts, health systems.
