Evidence map›Paper›PMID 40921907›Full record

ArticleArchives of osteoporosis2025

Multiple large language models versus clinical guidelines for postmenopausal osteoporosis: a comparative study of ChatGPT-3.5, ChatGPT-4.0, ChatGPT-4o, Google Gemini, Google Gemini Advanced, and Microsoft Copilot.

Chun-Ru Lin, Yi-Jun Chen, Po-An Tsai, Wen-Yuan Hsieh, Sung Huang Laurent Tsai, Tsai-Sheng Fu, Po-Liang Lai, Jau-Yuan Chen

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Archives of osteoporosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. OsteoCHAT: real-world patient evaluation and benchmarking of a guideline-grounded osteoporosis chatbot.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Chun-Ru Lin *Department of Orthopaedic Surgery, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City, 333, Taiwan.
Yi-Jun Chen *Department of Anesthesiology, Chang Gung Memorial Hospital, Taoyuan City, Taiwan.
Po-An TsaiDepartment of internal medicine, Chang Gung Memorial Hospital Keelung Branch, Keelung, Taiwan.
Wen-Yuan HsiehDepartment of Medical Education, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City, 333, Taiwan.
Sung Huang Laurent TsaiDepartment of Orthopaedic Surgery, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City, 333, Taiwan.
Tsai-Sheng FuDepartment of Orthopaedic Surgery, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City, 333, Taiwan.
Po-Liang LaiDepartment of Orthopaedic Surgery, Chang Gung Memorial Hospital, Linkou Branch, No. 5, Fuxing Street, Guishan District, Taoyuan City, 333, Taiwan.
Jau-Yuan ChenDepartment of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City, Taiwan. welins@cgmh.org.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study assesses the performance of AI models in evaluating postmenopausal osteoporosis. We found that ChatGPT-4o produced the most appropriate responses, highlighting the potential of AI to enhance clinical decision-making and improve patient care in osteoporosis management. PURPOSE: The rise of artificial intelligence (AI) offers the potential for assisting clinical decisions. This study aims to assess the accuracy of various artificial intelligence models in providing recommendations for the diagnosis and treatment of postmenopausal osteoporosis.

methodsUsing questions from the 2020 American Association of Clinical Endocrinologists (AACE) guidelines for osteoporosis, AI models including ChatGPT-3.5, ChatGPT-4.0, ChatGPT-4o, Gemini, Gemini Advanced, and Copilot were prompted. Responses were classified as accurate if they did not contradict the clinical guidelines. Two additional categories, over-conclusive and insufficient, were created to further evaluate responses. Over-conclusive was designated if AI models provided recommendations not specified in the guidelines, while insufficient indicated a failure to provide relevant information included in the guidelines. Chi-square tests were employed to compare categorical outcomes among different AI models.

resultsA total of 42 clinical questions were evaluated. ChatGPT-4o achieved an accuracy of 88%, ChatGPT-3.5 57.1%, ChatGPT-4.0 64.3%, Gemini 45.2%, Gemini Advanced 57.1%, and Copilot 47.6% (p < 0.001).

conclusionsThe study reveals significant response accuracy variations across each AI model, with ChatGPT-4o demonstrating the highest accuracy. Further research is necessary to explore the broader applicability of AI in the medical domains.

Indexed as

Artificial IntelligenceOsteoporosis, PostmenopausalPractice Guidelines as TopicClinical Decision-MakingFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsArtificial intelligenceChatGPTPostmenopausal osteoporosis

Identifiers

PMID40921907

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.