Evidence map›Paper›PMID 41070078›Full record

ArticleFrontiers in medicine2025

Performance of GPT-4 for planning acupuncture treatment: comparison with human clinician performance.

Da-Eun Yoon, Cheol-Han Kim, Yeonhee Ryu, Ye-Seul Lee, Younbyoung Chae

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Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Da-Eun YoonDepartment of Meridian and Acupoint, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.
Cheol-Han KimDepartment of Meridian and Acupoint, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.
Yeonhee RyuKM Fundamental Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea.
Ye-Seul LeeJaseng Spine and Joint Research Institute, Jaseng Medical Foundation, Seoul, Republic of Korea.
Younbyoung ChaeDepartment of Meridian and Acupoint, College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The medical knowledge of GPT-4 has been evaluated on patient data, providing diagnostic and treatment suggestions. However, few studies have directly compared the clinical suggestions of GPT-4 with those of groups of practitioners. Methods: This study assessed the ability of GPT-4 to make medical decisions regarding acupuncture treatment by comparing its selection of acupoints with those made by human clinicians. Ten case reports published in Korean medical journals were selected and put in a standardized format. The standardized patient information was given to 80 Korean Medicine doctors and GPT-4 to diagnose and prescribe three to five acupoints per case. To evaluate the performance of GPT-4, the similarities in acupoint selection between the doctors and GPT-4 were quantified based on the percentage overlap and correlations of the selection probabilities of acupoints in each case. Results: The average percentage overlap for acupoints among cases at the 10% cutoff was 51.3%, i.e., more than half of the GPT-4 acupoint suggestions overlapped the acupoints selected by the doctors. In half of the cases, significant correlations were observed in the acupoint selection probabilities, implying that GPT-4 acupoint suggestions are similar to those of doctors. Conclusions: GPT-4 made reasonable acupoint suggestions, with notable overlap observed with the prescriptions of doctors. This shows its promise for supporting medical decisions, education, and personalized medicine for patients undergoing acupuncture treatment. Future studies and validation are necessary to ensure the reliability and efficacy of applying GPT-4 in real-world settings.

Indexed as

acupoint selectionartificial intelligenceGPT-4large language modelmedical decision-making

Identifiers

PMID41070078
PMCPMC12504477

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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 OpenQuestion graph.