Masumeh Saeidi Health Providers 2820-9192 6 1 2026 06 01 Artificial Intelligence in Health Systems: Global Trends and Iran’s Position; A Narrative Review 246228 10.22034/hp.2026.588569.1086 EN Mohammad Vahedian-shahroodi Professor of Health Education and Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. 0000-0002-5402-1646 Mehdi Sedaghatkar Electronics Engineer, Mashhad Municipality Fire and Safety Services Organization, Mashhad, Iran. Kobra Sadat Hossaini MSc of Midwifery, Student Research Committee, School of Midwifery Nursing, Mashhad University of Medical Sciences, Mashhad, Iran. 0009-0004-3240-4728 Alireza Akhlaghi MSc in Health Education and Promotion, Bushehr University of Medical Sciences, Bushehr, Iran. 0000-0003-1662-6174 Journal Article 2026 06 25 Artificial intelligence (AI) has emerged as one of the most transformative technologies in health systems, with expanding applications in disease diagnosis, prediction of clinical outcomes, personalized medicine, drug discovery, and health service management. This narrative review examined the current evidence on the applications, opportunities, and challenges of AI in health systems and analyzed Iran’s position relative to global trends. Sources were searched in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar for studies published between January 2015 and June 2025. Relevant studies were selected and thematically synthesized across major domains, including clinical applications, managerial uses, ethical and legal considerations, and implementation challenges. The findings suggest that AI has made substantial contributions to global health care, particularly in diagnostic imaging, outcome prediction, personalized care, drug development, and system-level efficiency. However, persistent barriers remain, including data quality and generalizability, algorithmic transparency, bias, privacy concerns, ethical issues, and the absence of robust regulatory frameworks. In Iran, despite notable scientific and academic capacity, practical implementation remains constrained by fragmented health data, limited integrated health information infrastructure, and a gap between research and real-world deployment. AI can improve quality, access, and efficiency in health care, but effective use requires high-quality data infrastructure, clear ethical and legal frameworks, and stronger interdisciplinary collaboration among clinicians, data scientists, engineers, and health policymakers.