ReviewDigital health
A bibliometric analysis of the global research landscape on artificial intelligence applications in clinical medicine (2010-2025).
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI in pharmacy education: a comparative visualization analysis of global and Chinese research trends.Frontiers in medicine · 2026Pooled it
- Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).Frontiers in endocrinology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: In the digital era, artificial intelligence (AI) is increasingly used in clinical medicine. To investigate this trend, this study uses bibliometric methods to systematically review the literature on AI applications in clinical medicine from 2010 to 2025, aiming to reveal the global landscape of development. Methods: This study employs bibliometric analysis methods based on the Web of Science Core Collection database, utilizing software such as Microsoft Office Excel 2023, Origin, VOSviewer, CiteSpace, and Bibliometrix to analyze the selected literature and identify research trends and hotspots in the application of AI within clinical medicine. Results: A total of 2,872 literature articles on AI applications in clinical medicine were included in the analysis. Since 2017, publication volume has increased significantly. Researchers from 114 countries contributed to this field. The United States produced the highest number of articles and led in international collaborations. In total, 1,000 institutions were engaged in AI clinical medicine research, with Harvard Medical School having the highest output (n = 85). 19,537 researchers contributed to the publication of the research report. Arman Rahmim from the University of British Columbia was the most prolific (n = 12), maintaining high productivity between 2020 and 2022. The fields of medicine, general medicine, and internal medicine dominated participation in AI clinical applications. Biomedical sciences showed the highest level of involvement (n = 798). Currently, AI, classification, and prediction studies are at the forefront of AI clinical applications. In 2023, the emergence of ChatGPT, a large language model, brought this technology to the forefront. Conclusion: AI fosters rapid growth in global research within clinical medicine. This expansion is driven by technological innovation and spreads across all areas of healthcare. Large language models, such as ChatGPT, have initiated a new growth phase in this field. Their integration with clinical scenarios is accelerating intelligent and convergent advancements.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.