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Evaluation of large language models in patient education for hyperthyroidism: A comparative study of chatgpt, gemini, and deepseek.

作者信息

Hu Xin, Lin Ruhai, Huang Yinqiong, Wu Xiaohong, Gong Yiran, Huang Huibin

机构信息

Department of Endocrinology, the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.

Department of General Medicine, the First Affiliated Hospital of Fujian Medical University, Fuzhou, China.

出版信息

Endocrine. 2025 Sep 13. doi: 10.1007/s12020-025-04421-6.

DOI:10.1007/s12020-025-04421-6
PMID:40944884
Abstract
摘要

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DeepSeek in Healthcare: Revealing Opportunities and Steering Challenges of a New Open-Source Artificial Intelligence Frontier.医疗保健领域的DeepSeek：揭示新开源人工智能前沿的机遇与导向挑战
Cureus. 2025 Feb 18;17(2):e79221. doi: 10.7759/cureus.79221. eCollection 2025 Feb.
2
Larger and more instructable language models become less reliable.更大且更具指导性的语言模型变得不那么可靠。
Nature. 2024 Oct;634(8032):61-68. doi: 10.1038/s41586-024-07930-y. Epub 2024 Sep 25.
3
Large Language Model Prompting Techniques for Advancement in Clinical Medicine.
促进临床医学发展的大语言模型提示技术。
J Clin Med. 2024 Aug 28;13(17):5101. doi: 10.3390/jcm13175101.
4
Using artificial intelligence to generate medical literature for urology patients: a comparison of three different large language models.利用人工智能为泌尿科患者生成医学文献：三种不同的大型语言模型比较。
World J Urol. 2024 Jul 29;42(1):455. doi: 10.1007/s00345-024-05146-3.
5
Evaluating prompt engineering on GPT-3.5's performance in USMLE-style medical calculations and clinical scenarios generated by GPT-4.评估 GPT-3.5 在 USMLE 式医学计算和由 GPT-4 生成的临床场景中的表现的提示工程。
Sci Rep. 2024 Jul 28;14(1):17341. doi: 10.1038/s41598-024-66933-x.
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Assessing large language models' accuracy in providing patient support for choroidal melanoma.评估大型语言模型在为脉络膜黑色素瘤患者提供支持方面的准确性。
Eye (Lond). 2024 Nov;38(16):3113-3117. doi: 10.1038/s41433-024-03231-w. Epub 2024 Jul 13.
7
Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication.大语言模型在围手术期风险预测和预后中的应用。
JAMA Surg. 2024 Aug 1;159(8):928-937. doi: 10.1001/jamasurg.2024.1621.
8
Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes.大语言模型中提示工程与微调策略在临床记录分类中的比较
AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:478-487. eCollection 2024.
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