ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
This article is part of the Research TopicFuture Horizons in Diabetes: Integrating Gut Microbiota, AI, and Personalized CareView all 11 articles
Efficacy of ChatGPT in Personalized Glucose-Lowering Strategy Development: A Clinician-Based Comparative Study
Provisionally accepted
Yu WANG1,2
Chenglin ZHANG3
Huijuan ZHAO1Chang WANG4Lin GUO1
Pengfei Wei1Mingyue JIN1Aiping LI1
Qiang Li1*Hongyan PAN1*- 1Department of Endocrinology and Metabolism, Shenzhen University General Hospital, Shenzhen, China
- 2Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, Hong Kong, SAR China
- 3Department of Pathophysiology, Shenzhen University, Shenzhen, China
- 4Department of Nephrology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
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Background: The increasing incidence of diabetes poses a significant burden on healthcare systems. Limited research exists on tools to assist providers in developing personalized glucose-lowering strategies, which could alleviate this pressure and enhance patient outcomes. Objective: This study aims to evaluate the capability of ChatGPT-4o in developing personalized glucose-lowering strategies for individuals with diabetes. Methods: First, an evaluation of ChatGPT-4o's performance on China's qualification examination for attending physicians in endocrinology. Second, a cross-sectional study was conducted, involving the comparison of glucose-lowering strategies formulated by ChatGPT-4o, general practitioners (GPs), and attending physicians (APs) in endocrinology for a set of 30 real-world diabetes cases. Three clinical experts scored blindly the reasonableness of each strategy on a scale, with stratification of cases into three complexity levels (A, B, and C) and evaluation of mean scores for each level. Results: ChatGPT-4o successfully passed all sections of the qualification examination with scores above the 60% threshold. In developing glucose-lowering strategies, ChatGPT-4o achieved a mean score comparable to GPs (82.24 ± 9.933 vs 79.83 ± 3.768; p = .317) but lower than APs (82.24 ± 9.933 vs 86.35 ± 4.142; p = .0467). Performance declined with increasing case complexity, with mean This is a provisional file, not the final typeset article scores dropping from 89.90 ± 2.936 for simple cases (A-level) to 76.12 ± 11.93 for complex cases (C-level) (p < .0020). Conclusions: ChatGPT-4o performs reliably in generating glucose-lowering strategies for simpler diabetes cases, highlighting its potential to assist community health workers. However, its accuracy in complex cases, especially concerning medication contraindications, requires improvement.
Keywords: Attending physicians, ChatGPT, comparative study, General Practitioners, Large Language Model, personalized glucose-lowering strategy
Received: 27 Aug 2025; Accepted: 05 Feb 2026.
Copyright: © 2026 WANG, ZHANG, ZHAO, WANG, GUO, Wei, JIN, LI, Li and PAN. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qiang Li
Hongyan PAN
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