A Bibliometric Analysis of Artificial Intelligence in Medicine (2019-2023)
The integration of artificial intelligence (AI) into medicine has inaugurated an era of unprecedented innovation, profoundly impacting healthcare delivery and patient outcomes. AI, leveraging advanced computer algorithms, interprets information, analyzes data, and performs tasks such as decision-making and data interpretation, akin to human cognitive processes. This transformative power has significantly shifted medical practice from traditional methods towards digital healthcare.
To thoroughly understand the current development, primary research focuses, and key contributors in the field of AI applications in medicine, a comprehensive bibliometric analysis was conducted. This study systematically reviewed and quantified the growth of scientific literature on AI in healthcare over a five-year period.
Methods: The research utilized the Web of Science Core Collection (WoSCC) database as its main data source, covering the period from January 2019 to December 2023. Bibliometric analysis and network visualization were performed using VOSviewer and R-bibliometrix. The analysis encompassed various parameters, including the number of publications, contributing countries, journals, citations, authors, and keywords.
Key Findings:
- A total of 1,811 publications on AI in medicine were released across 565 journals by 12,376 authors affiliated with 3,583 institutions from 97 countries.
- There has been a steady and rapid rise in publications related to AI in medicine since 2019, indicating significant growth in the field, with an average annual increase of 28.4%.
- The United States emerged as the foremost producer of scholarly works, demonstrating significant impact and leadership in both publication volume (709 papers) and total citations (14,764).
- Among institutions, Harvard Medical School recorded the highest publication count.
- The Journal of Medical Internet Research stood out with the highest H-index (19), publication count (76), and total citations (1,495).
- Four distinct keyword clusters were identified, highlighting core research areas: AI applications in digital health, COVID-19 and ChatGPT, precision medicine, and public health epidemiology.
- Keywords such as “Outcomes” and “Risk” exhibited a notable upward trend, indicating their prominence as future research focal points.
- The study revealed strong global cooperation in the field, with leading collaborative nations including the United States, China, England, Germany, and Italy, though a call for even more effective cross-regional and international cooperation was noted.
Conclusion: This bibliometric study offers crucial insights into the current landscape, collaborative frameworks, and key research topics in AI for medicine, pointing towards potential future research directions. It is foreseeable that AI will increasingly play a pivotal role in digital health and public health, significantly enhancing disease forecasting, identification, diagnosis, categorization, treatment, and survival prediction, thereby promoting a sustainable approach to precision medicine and improving patients’ quality of life.
Reference for this Article:
Lin, M., Lin, L., Lin, L., Lin, Z., & Yan, X. (2025). A bibliometric analysis of the advance of artificial intelligence in medicine. Frontiers in Medicine, 12, 1504428. https://doi.org/10.3389/fmed.2025.1504428
