Artificial intelligence (AI) has emerged as a transformative technology in modern medical treatment, providing significant advances in disease diagnosis, clinical decision-making, medical imaging, drug discovery, and personalized healthcare. However, the rapid integration of AI into healthcare systems has generated various social and ethical challenges, including concerns related to patient privacy, algorithmic bias, transparency, accountability, data governance, and regulatory uncertainty. These challenges have created an urgent need to understand the global research landscape regarding the responsible implementation and governance of AI in medical treatment.

This study aims to investigate the development trends, knowledge structures, research hotspots, and emerging frontiers in the field of social risks and regulatory governance of artificial intelligence in medical treatment through a CiteSpace-based bibliometric analysis. Publications related to AI, healthcare risks, ethics, and regulation were retrieved from the Web of Science Core Collection database. CiteSpace software was applied to conduct scientific mapping, including publication trend analysis, country and institutional collaboration analysis, keyword co-occurrence analysis, citation analysis, and burst detection.

The findings demonstrate that research on AI governance in medical treatment has expanded rapidly, particularly after 2015, with increasing attention toward ethical AI, trustworthy artificial intelligence, explainable AI, healthcare data protection, and regulatory frameworks. Early research mainly concentrated on technological development and clinical applications, while recent studies have shifted toward social responsibility, governance mechanisms, and international regulatory cooperation. This study provides a comprehensive understanding of global research evolution and highlights future directions for developing safe, transparent, and human-centered artificial intelligence systems in healthcare.

Artificial Intelligence, Medical Treatment, Healthcare AI, Social Risk, Regulatory Governance, CiteSpace, Bibliometric Analysis interoperability, cyber

Chen C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology. 2006;57(3):359–377.

Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. 2019;25:44–56.

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature Biomedical Engineering. 2018;2:719–731.

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. New England Journal of Medicine. 2019;380:1347–1358.

Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nature Medicine. 2019;25:24–29.

Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine. 2019;17:195.

Wiens J, Saria S, Sendak M, et al. Do no harm: A roadmap for responsible machine learning for healthcare. Nature Medicine. 2019;25:1337–1340.

Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nature Machine Intelligence. 2019;1:389–399.

Morley J, Machado CCV, Burr C, et al. The ethics of AI in healthcare: A mapping review. Social Science & Medicine. 2020;260:113172.

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage health populations. Science. 2019;366:447–453.

World Health Organization. Ethics and governance of artificial intelligence for health. 2021.

European Commission. Artificial Intelligence Act: Regulatory framework for trustworthy AI. 2024.

Floridi L, Cowls J. A unified framework of five principles for AI in society. Harvard Data Science Review. 2019.

Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health. 2021;3:e745–e750.

Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence. Nature Medicine. 2020;26:1363–1367.