ReviewDigital health
Application of large language models in medical diagnosis: A bibliometric review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of Large Language Models into medical diagnosis represents an emerging field with the potential to support diagnostic workflows across diverse clinical settings. However, the trends and evolutionary trajectory of LLM-assisted diagnostic research remain insufficiently understood. Objective: This bibliometric review aims to map the global research landscape, identify key research clusters, and analyze the development trajectory of LLM technologies in medical diagnosis, with an emphasis on descriptive synthesis rather than formal evaluation. Methods: A bibliometric analysis was conducted on relevant publications retrieved from the Web of Science Core Collection, covering the period from Q1 2023 to Q1 2025. The extracted data were processed and visualized using Excel, ArcGIS, VOSviewer, CiteSpace, and Pajek. The analyses included publication trends, influential authors and institutions, collaboration networks, and research cluster mapping. Results: A total of 650 publications were included in the analysis. Research output increased markedly from Q1 2023 onward, rising from 2 publications to 148 by Q1 2025, corresponding to an average quarterly growth rate of 71.25%. The United States (273 publications), China (135 publications), and Germany (65 publications) emerged as the leading contributing countries. The three most productive institutions were all based in the United States: Harvard University (26 publications), Stanford University (26 publications), and the Icahn School of Medicine at Mount Sinai (20 publications). Keyword co-occurrence analysis identified 10 core clusters, with a modularity Q value of 0.8231 and a silhouette S value of 0.9412, indicating a highly coherent clustering structure and strong internal consistency. Conclusion: The development of LLM technologies has substantially influenced the research landscape of medical diagnostics. As this field continues to evolve, it is crucial to refine model performance, integrate multimodal data, and address ethical considerations. Future research should focus on optimizing LLMs for specific clinical applications and evaluating their implementation in real-world healthcare settings.
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.