Effectiveness of various general large language models in clinical consensus and case analysis in dental implantology: a comparative study.

IF 3.3 3区医学 Q2 MEDICAL INFORMATICS

BMC Medical Informatics and Decision Making Pub Date : 2025-03-26 DOI:10.1186/s12911-025-02972-2

Yuepeng Wu, Yukang Zhang, Mei Xu, Chen Jinzhi, Yican Xue, Yuchen Zheng

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引用次数: 0

Abstract

Background: This study evaluates and compares ChatGPT-4.0, Gemini Pro 1.5(0801), Claude 3 Opus, and Qwen 2.0 72B in answering dental implant questions. The aim is to help doctors in underserved areas choose the best LLMs(Large Language Model) for their procedures, improving dental care accessibility and clinical decision-making.

Methods: Two dental implant specialists with over twenty years of clinical experience evaluated the models. Questions were categorized into simple true/false, complex short-answer, and real-life case analyses. Performance was measured using precision, recall, and Bayesian inference-based evaluation metrics.

Results: ChatGPT-4 exhibited the most stable and consistent performance on both simple and complex questions. Gemini Pro 1.5(0801)performed well on simple questions but was less stable on complex tasks. Qwen 2.0 72B provided high-quality answers for specific cases but showed variability. Claude 3 opus had the lowest performance across various metrics. Statistical analysis indicated significant differences between models in diagnostic performance but not in treatment planning.

Conclusions: ChatGPT-4 is the most reliable model for handling medical questions, followed by Gemini Pro 1.5(0801). Qwen 2.0 72B shows potential but lacks consistency, and Claude 3 Opus performs poorly overall. Combining multiple models is recommended for comprehensive medical decision-making.

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来源期刊

BMC Medical Informatics and Decision Making 医学-医学：信息

CiteScore

7.20

自引率

5.70%

发文量

297

审稿时长

1 months

期刊介绍： BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.