应用ChatGPT作为持续医学教育中的内容生成工具:作为测试主题
Luigi Naldi1, Vincenzo Bettoli2, Eugenio Santoro3
1Dermatology Unit, Ospedale San Bortolo, Vicenza; Centre of the Italian Group for Epidemiological Research in Dermatology (GISED), Bergamo. luiginaldibg@gmail.com.
Dermatology reports
|February 19, 2025
概括
像ChatGPT这样的大型语言模型 (LLM) 对医学教育有希望,但专家监督至关重要. 需要严格的评估,以确保人工智能生成的医疗内容是准确的,并与临床指南保持一致.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 医疗教育 技术 技术 医学教育
背景情况:
- 大型语言模型 (LLM) 展示了回答复杂问题的能力.
- 评估人工智能生成的医疗信息的可靠性对于临床应用至关重要.
- 该AICHECK (CME健康电子学习内容和知识的人工智能) 研究调查了持续医学教育 (CME) 中的LLM潜力.
研究的目的:
- 评估ChatGPT生成的教育内容的准确性和一致性,与既定医疗指南相比.
- 评估人工智能生成的医疗信息的质量,可读性,彻底性和参考质量.
- 确定人工智能生成的答案和引用的内部可复制性.
主要方法:
- 聊天GPT版本4使用皮肤科医生开发的23项关于的问卷进行了查询.
- 一个由五名皮肤科医生组成的小组根据预定义的标准评估了人工智能产生的反应.
- 评级包括质量,可读性,准确性,彻底性,与NICE指南的一致性以及参考相关性,相关性和更新状态.
主要成果:
- 皮肤科医生对ChatGPT的质量 (87.8%),可读性 (94.8%) 和彻底性 (85.2%) 的答案进行了高度评价.
- 准确性 (75.7%) 和与NICE指南 (76.8%) 的一致性较低,特别是在有争议的话题上.
- 参考文献在相关性 (94.6%) 和相关性 (91.2%) 方面被评为高,但在最新性 (62.3%) 方面被评为低. 对于答案 (93.5%) 的内部可复制性足够,但对于参考 (67.4%) 的内部可复制性较低.
结论:
- 聊天GPT展示了产生可访问和全面的医学教育内容的潜力.
- 专家评估和监督对于确保人工智能生成的医疗信息的准确性和遵守指南至关重要.
- 需要进一步开发医疗教育中人工智能的严格评估框架.
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