Evaluate the accuracy of ChatGPT’s responses to diabetes questions and misconceptions-Reference-Cited by-同舟云学术

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Evaluate the accuracy of ChatGPT’s responses to diabetes questions and misconceptions

Published:2023-07-26 Issue:1 Volume:21 Page:
ISSN:1479-5876
Container-title:Journal of Translational Medicine
language:en
Short-container-title:J Transl Med

Author:

Huang Chunling,Chen Lijun,Huang Huibin^ORCID,Cai Qingyan,Lin Ruhai,Wu Xiaohong,Zhuang Yong,Jiang Zhengrong

Funder

the startup fund for scientific research, Fujian Medical University

Fujian Provincial Young and Middle-aged Teachers' Education Research Project

Publisher

Springer Science and Business Media LLC

Subject

General Biochemistry, Genetics and Molecular Biology,General Medicine

Link

https://link.springer.com/content/pdf/10.1186/s12967-023-04354-6.pdf

Reference4 articles.

1. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88–98.

2. Radford A, Narasimhan K. Improving language understanding by generative pre-training. 2018.

3. Mathur K, et al. Effect of artificial sweeteners on insulin resistance among type-2 diabetes mellitus patients. J Fam Med Primary Care. 2020;9(1):69–71.

4. Zhou X, Zeng C. Diabetes remission of bariatric surgery and nonsurgical treatments in type 2 diabetes patients who failure to meet the criteria for surgery: a systematic review and meta-analysis. BMC Endocr Disord. 2023;23(1):46.

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2. Evaluating the accuracy and adequacy of ChatGPT in responding to queries of diabetes patients in primary healthcare;International Journal of Diabetes in Developing Countries;2024-09-11

3. Evaluating accuracy and reproducibility of ChatGPT responses to patient-based questions in Ophthalmology: An observational study;Medicine;2024-08-09

4. Appropriateness of Artificial Intelligence Chatbots in Diabetic Foot Ulcer Management;The International Journal of Lower Extremity Wounds;2024-02-28

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