ORIGINAL RESEARCH article
Front. Public Health
Sec. Digital Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1605908
This article is part of the Research TopicAdvancing Healthcare AI: Evaluating Accuracy and Future DirectionsView all 6 articles
Battle of the artificial intelligence: a comprehensive comparative analysis of DeepSeek and ChatGPT for urinary incontinence-related questions
Provisionally accepted
Huawei Cao1
Changzhen Hao2Tao Zhang3
Xiang Zheng1Zihao Gao1
Jiyue Wu1Lijian Gan1Yu Liu3*
Xiangjun Zeng4*
Wei Wang1*- 1Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
- 2International Hospital, Peking University, Beijing, Beijing Municipality, China
- 3Department of Pathology, Basic medical school, Capital Medical University, Beijing, China
- 4Department of Physiology and Pathophysiology, Basic Medical School, Capital Medical University, Beijing, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: With the rapid advancement and widespread adoption of artificial intelligence (AI), patients increasingly turn to AI for initial medical guidance. Therefore, a comprehensive evaluation of AI-generated responses is warranted. This study aimed to compare the performance of DeepSeek and ChatGPT in answering urinary incontinence-related questions and to delineate their respective strengths and limitations.Methods: Based on the American Urological Association/Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction (AUA/SUFU) and European Association of Urology (EAU) guidelines, we designed 25 urinary incontinence-related questions. Responses from DeepSeek and ChatGPT-4.0 were evaluated for reliability, quality, and readability. Fleiss' kappa was employed to calculate inter-rater reliability. For clinical case scenarios, we additionally assessed the appropriateness of responses. A comprehensive comparative analysis was performed.The modified DISCERN (mDISCERN) scores for DeepSeek and ChatGPT-4.0 were 28.24 ± 0.88 and 28.76 ± 1.56, respectively, showing no practically meaningful difference [P = 0.188, Cohen's d = 0.41 (95% CI: -0.15, 0.97)]. Both AI chatbots rarely provided source references. In terms of quality, DeepSeek achieved a higher mean Global Quality Scale (GQS) score than ChatGPT-4.0 (4.76 ± 0.52 vs. 4.32 ± 0.69, P = 0.001). DeepSeek also demonstrated superior readability, as indicated by a higher Flesch Reading Ease (FRE) score (76.43 ± 10.90 vs. 70.95 ± 11.16, P = 0.039) and a lower Simple Measure of Gobbledygook (SMOG) index (12.26 ± 1.39 vs. 14.21 ± 1.88, P < 0.001), suggesting easier comprehension. Regarding guideline adherence, DeepSeek had 11 (73.33%) fully compliant responses, while ChatGPT-4.0 had 13 (86.67%), with no significant difference [P = 0.651, Cohen's w = 0.083 (95% CI: 0.021, 0.232)].DeepSeek and ChatGPT-4.0 might exhibit comparable reliability in answering urinary incontinence-related questions, though both lacked sufficient references. However, DeepSeek outperformed ChatGPT-4.0 in response quality and readability. While both AI chatbots largely adhered to clinical guidelines, occasional deviations were observed. Further refinements are necessary before the widespread clinical implementation of AI chatbots in urology.
Keywords: artificial intelligence, Urinary Incontinence, ChatGPT, deepseek, comparative analysis
Received: 07 Apr 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Cao, Hao, Zhang, Zheng, Gao, Wu, Gan, Liu, Zeng and Wang. 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:
Yu Liu, Department of Pathology, Basic medical school, Capital Medical University, Beijing, 100069, China
Xiangjun Zeng, Department of Physiology and Pathophysiology, Basic Medical School, Capital Medical University, Beijing, 100069, China
Wei Wang, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.