Previously submitted to: Journal of Medical Internet Research (no longer under consideration since May 08, 2025)
Date Submitted: Dec 12, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Discovering Topics and Trends in Artificial Intelligence Chatbots in Medicine: Using Latent Dirichlet Allocation Topic Modeling
ABSTRACT
Background:
With the widespread adoption of the internet and smart devices, chatbots have emerged as significant auxiliary tools for public health activities. Despite the increasing application of chatbots in the medical field, comprehensive assessments of research topics and trends in this area remain relatively scarce.
Objective:
This study analyzed the application topics of chatbot technology in the medical field and explored the trends of these topics across different time periods, various journals, and different countries.
Methods:
In this study, a bibliometric approach was used to systematically search the PubMed, CINAHL, Web of Science and Embase databases for literature on medicine and chatbots between 2004 and 2024. By applying Latent Dirichlet Allocation (LDA) topic modeling, the study identified and analyzed the thematic applications of chatbots in the medical field, and explored the temporal evolution of these topics as well as their distribution characteristics across journals and countries.
Results:
We ultimately identified 3,029 articles for analysis. Utilizing the Latent Dirichlet Allocation (LDA) topic modeling technique, we identified nine core topics from the abstracts: ChatGPT medical quiz accuracy research, digital healthcare support assistants, mental health intervention research, epidemic health conversation application research, cancer patient diagnosis and treatment care, artificial intelligence (AI) healthcare education potential research, natural language processing models, human-computer interaction emotion research, and AI reading assistance systems. This study also found that these topics have shown diverse developmental trajectories over time, reflecting the evolution of research interests. In addition, researchers from different journals and countries have shown significant differences in the topics they focus on.
Conclusions:
This study analyzed the topic distribution, temporal trends, journal, and country distribution characteristics of chatbots in the medical field. The results revealed popular and less researched topics, as well as emerging directions and trends, providing researchers with a tool for rapid identification. These findings not only provide guidance for researchers in selecting research directions but also offer references for journals and countries in determining research priorities, formulating strategic plans, and promoting international collaborative research.
Citation
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Copyright
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