Artificial Intelligence and Deep Learning are increasingly being used to support the prevention, diagnosis, and follow-up of non-communicable diseases, helping to ease the burden on under-resourced healthcare systems.

Present day healthcare systems face several complex challenges, including rising demand due to an aging population, increasing prevalence of chronic and complex conditions, rising costs, and shortages in the healthcare workforce. Artificial intelligence (AI) has the potential to address some of these by improving operational efficiency, reducing administrative burdens, and enhancing diagnosis and treatment pathways. Despite the promise and availability AI-based tools in the market, their deployment in clinical practice is slow. Using a mixed methods approach, entailing a literature review and consultation activities, the study identifies a range of challenges to AI deployment in healthcare, spanning technological and data-related issues, legal and regulatory complexities, organisational and business challenges, and social and cultural barriers. It also highlights successful strategies (accelerators) employed by hospitals globally to overcome these common obstacles, offering valuable inspiration in the broader European Union (EU) context. The EU is uniquely positioned to support the safe, effective, ethical and equitable scale-up of AI deployment in healthcare, balancing the need to nurture innovation with safeguarding the fundamental rights of patients. This report presents considerations for future action and proposes a monitoring and indicators framework that could enable progress to be tracked with the view of enabling the sustainable integration of AI into healthcare systems.