利用大型语言模型为儿科慢性病护理提供信息:一项横断面研究。

IF 2.3 4区 医学 Q2 PEDIATRICS
Syed Furrukh Jamil, Nada N Alshathri, Seham S Alsalamah, Nura A Almansour, Faris S Alsalamah, Tahir K Hameed, Jubran T Alqanatish
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引用次数: 0

摘要

本研究评估了ChatGPT 3.5、ChatGPT 4.0和谷歌Gemini在提供有关乳糜泻和1型糖尿病的教育内容方面的表现。我们分析了76个常见问题的准确性、全面性、可读性和一致性。这些模型提供了高度准确和全面的全面反应。虽然ChatGPT 4.0提供了最易读的内容,但所有模型都在总体可读性上挣扎。每个模型在整个测试过程中都保持一致的性能。这些结果表明,大型语言模型有望作为慢性儿科疾病患者教育的补充工具,尽管需要提高可读性以增强可访问性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging large language models to inform paediatric chronic condition care: a cross-sectional study.

This study assessed how ChatGPT 3.5, ChatGPT 4.0 and Google Gemini perform in providing educational content about coeliac disease and type 1 diabetes mellitus. We analysed 76 frequently asked questions for accuracy, comprehensiveness, readability and consistency. The models delivered highly accurate and comprehensive responses across the board. While ChatGPT 4.0 offered the most readable content, all models struggled with overall readability. Each model maintained consistent performance throughout testing. These results indicate that large language models show promise as supplementary tools for patient education in chronic paediatric conditions, though improvements in readability are needed to enhance accessibility.

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来源期刊
BMJ Paediatrics Open
BMJ Paediatrics Open Medicine-Pediatrics, Perinatology and Child Health
CiteScore
4.10
自引率
3.80%
发文量
124
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