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This paper is in the following e-collection/theme issue:

Applications of AI (1154) Chatbots and Conversational Agents (1267) Artificial Intelligence (5153) Digital Health Meta-Research and Bibliographic Studies (123) Theme Issue: ChatGPT and Generative Language Models in Medical Education (144) Generative Language Models Including ChatGPT (1700) AI Language Models in Health Care (875)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68603, first published .
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Identification and Categorization of the Top 100 Articles and the Future of Large Language Models: Thematic Analysis Using Bibliometric Analysis

Identification and Categorization of the Top 100 Articles and the Future of Large Language Models: Thematic Analysis Using Bibliometric Analysis

Authors of this article:

Ethan Bernstein1 Author Orcid Image ;   Anya Ramsamooj1 Author Orcid Image ;   Kelsey L Millar2 Author Orcid Image ;   Zachary C Lum2 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Bernstein E, Bol B, Shanaa J, Ramsamooj A, Baldini T, Lum Z. Evaluating the Citation Accuracy of ChatGPT-5 Using the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines. Cureus 2026 View
  2. Güneş Z, Mergen E. Evolution of Artificial Intelligence Adoption in American Journal of Sports Medicine Abstracts: A Period and Nativity-Based Analysis. Orthopaedic Journal of Sports Medicine 2026;14(9) View
  3. Ayaz R, Özköse H, Beri̇gel M. From Big Data to Generative Artificial Intelligence: A Bibliometric Analysis of the Conceptual Structure and Thematic Evolution of Computational Social Science (2011–2025). Information Systems Frontiers 2026 View