Background: Patient education faces challenges including health literacy disparities and resource constraints. Artificial intelligence (AI) offers transformative potential through personalized, accessible tools, yet a comprehensive bibliometric analysis of this rapidly evolving field is lacking. Methods: We analyzed 428 English-language articles and reviews from 1995 to July 2025 retrieved from Web of Science using search terms such as “artificial intelligence” and “patient education” and conducted quantitative and network analysis with VOSviewer, CiteSpace, and bibliometrix, focusing on key metrics including research directions, publication trends, countries/regions, institutions, authors, journals, and keywords. Results: “Surgery” was the dominant research category (21.03%, centrality = 0.51). Publication growth surged exponentially post-2023 (189 publications in 2024). The United States led in output (221 publications), citations (3,033), and institutional contributions. Journal of Medical Internet Research was the core journal (29 papers). Keyword analysis revealed current hotspots centered on generative AI (e.g., “ChatGPT,” “large language models”) and information quality (e.g., “readability,” “health literacy”). Future trends indicate shifts toward personalized predictive tools and addressing ethical barriers (e.g., keyword bursts: “prediction,” “barriers” 2024–2025). Conclusion: The application of AI in patient education is advancing rapidly. This bibliometric analysis identifies growth trends, key contributors, and major research directions. It pinpoints current research hotspots while also indicating possible future trends. These insights aim to guide future interdisciplinary research.
Guo et al. (2025) studied this question.