Tehran University of Medical Sciences Acta Medica Iranica 0044-6025 64 4 2026 07 05 225 237 Masoud Imani Student Research Committee, Iran University of Medical Sciences, Tehran, Iran. AND Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran Nima Daneshi Department of Public Health, Behbahan Faculty of Medical Sciences, Behbahan, Iran. Nima Yousefi Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. Milad Mirzajana Sangari Department of Nutrition, School of Public Health, Iran University of Medical Sciences, Tehran, Iran. Masoud Salehi Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran. Abbas Harati Department of Medical Library and Information Science, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran Morteza Mohammadzadeh Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran 2025 01 13 2025 07 07 Artificial intelligence-powered healthcare system enhances disease prediction, diagnosis, and therapy, providing advantages to patients and healthcare practitioners. In this regard, this study aims to analyze the evolution of artificial intelligence (AI) research within Iranian healthcare from 2000 to 2025, focusing on its implications for medical practice and patient outcomes. A bibliometric analysis was conducted using data retrieved from the Web of Science Core Collection. The analysis included publication trends, leading authors, institutions, and keyword dynamics, emphasizing the significance of machine learning and predictive analytics in clinical applications. Our findings reveal a significant 13.1-fold increase in AI-related publications over the past decade, underscoring AI's growing role in healthcare advancements in Iran. Islamic Azad University emerged as the leading institution, while key authors and collaborative networks were identified. The keyword analysis highlighted "Machine Learning" as the most frequent term, indicating a shift towards predictive analytics in medical research. The results emphasize the transformative potential of AI in enhancing clinical decision-making and patient care delivery systems. As AI continues to integrate into healthcare practices, it presents opportunities for improved patient outcomes. This study serves as a vital resource for practitioners and policymakers aiming to effectively harness AI's capabilities in the Iranian healthcare landscape. https://acta.tums.ac.ir/index.php/acta/article/view/11479 https://acta.tums.ac.ir/index.php/acta/article/download/11479/6042