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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Generative artificial intelligence in healthcare: a bibliometric analysis.

Xin Tian^1,2, Fanyu Meng^1,2, Haoxin Guo^1,2

¹Department of Information Center, The First Hospital of China Medical University, Shenyang, China.

International Journal of Surgery (London, England)

|February 18, 2026

Summary

Generative artificial intelligence (Gen AI) is rapidly transforming healthcare, with a surge in research focusing on clinical applications, education, and mental health. Key areas include surgical innovation and ethical considerations, guiding future development and policy.

Keywords:

bibliometric clinical diagnosis generative artificial intelligence healthcare medicine

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Area of Science:

Healthcare Technology
Artificial Intelligence
Bibliometrics

Background:

Generative artificial intelligence (Gen AI) integration into healthcare is rapidly expanding.
A bibliometric analysis of 1987 publications reveals significant growth since 2023.

Purpose of the Study:

To analyze the bibliometric landscape of Gen AI in healthcare.
Identify key trends, influential publications, and research collaborations.

Main Methods:

Bibliometric analysis of 1987 English publications from Web of Science Core Collection.
Analysis of publication trends, influential journals, countries, affiliations, authors, and research topics.

Main Results:

Publication numbers surged to 496 in 2023 and 1478 in 2024.
Journal of Medical Internet Research, USA, and Harvard University were most influential.
Research topics span clinical diagnosis, decision support, education, mental health, and surgery, with emphasis on technical and ethical challenges.

Conclusions:

Gen AI shows significant potential in healthcare, particularly in surgery.
Findings offer insights for developers, policymakers, and researchers.
Future research should address technical and ethical considerations in Gen AI healthcare applications.