人工智能引发的常见初级保健诊断的解释
Mafaz Kattih1, Max Bressler1, Logan R Smith1
1Morsani College of Medicine, University of South Florida, Tampa, FL.
PRiMER (Leawood, Kan.)
|November 21, 2024
概括
人工智能生成的医学解释可以改善患者的理解. 克劳德2和ChatGPT显示出比其他AI工具更好的可读性和可理解性,尽管实际使用需要更多的研究.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 人工智能 (AI) 可能比传统来源提供更清晰的医学解释.
- 人工智能生成的内容可以提高患者对医疗保健决策的理解和自主性.
研究的目的:
- 评估人工智能生成的患者教育材料的可读性和可理解性.
- 为了比较不同的人工智能模型在解释常见医疗诊断方面的有效性.
主要方法:
- 聊天GPT 3.5,谷歌巴德,拥抱聊天和克劳德2为七种常见的诊断产生了患者教育段落.
- 使用Flesch阅读易度 (FRE) 和Flesch-Kincaid等级水平 (FKGL) 评估可读性.
- 使用患者教育材料评估工具 (PEMAT) 评估了可理解性和可操作性.
主要成果:
- 克劳德2获得了最高的FRE (67.0) 和可理解性 (69%),FKGL为7.4.
- 聊天GPT获得了FRE 58.5,FKGL 9.3和69%的可理解性.
- 与克劳德2和ChatGPT相比,谷歌Bard和HuggingChat在可读性和可理解性方面的得分较低.
结论:
- 克劳德2和ChatGPT为人工智能生成的患者教育提供了卓越的可读性和可理解性.
- 需要进一步的研究来探索实际应用和对患者结果的影响.
- 限制包括人工智能模型的快速演变和一些评估工具的主观性质.
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