评估GPT-4产生的同情心与医生撰写的急诊部出院信的对比
Gal Ben-Haim1,2,3, Adva Livne3,4, Uri Manor3,5
1Department of Emergency Medicine, Sheba Medical Center, Ramat-Gan, Israel.
Digital health
|November 17, 2025
概括
像GPT-4这样的大型语言模型在生成同情的急诊部出院信件方面显示出显著的潜力. 这些人工智能生成的信件被医疗保健专业人员和患者都喜欢,而不是医生写的信件.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 临床沟通中的同情心提高了患者的结果,满意度和治疗坚持度.
- 关于大语言模型 (LLM) 在医疗环境中传达同情心的现实应用的研究有限.
- 这项研究调查了与医生写的相比,LLM生成的出院信的同情性质.
研究的目的:
- 为了比较由GPT-4生成的急诊部 (ED) 出院信所传达的同情心与医生撰写的同情心.
- 评估人工智能生成与医生生成的信件的整体质量,摘要的清晰度和建议的清晰度.
- 评估医疗保健专业人员和患者对人工智能和人为生成的解雇信之间的偏好.
主要方法:
- 在第三级ED中进行了一项回顾性,盲目的,比较性研究.
- 来自一个8小时班的ED放电信与使用ED笔记生成的GPT-4信件进行了比较.
- 十七位评价者 (医生,护士,患者) 在五分利克特尺度上对字母进行了同情,质量和清晰度的评分.
主要成果:
- 在83.7%的比较中,GPT-4生成的信件比医生信件更受青 (p < 0.001).
- 在GPT-4信件中,同情度 (中位数为4.0比3.0,p<0.001),总体质量和摘要清晰度的得分显著提高.
- 在对患者的建议清晰度方面没有发现显著差异 (p = 0.771).
结论:
- GPT-4显示出强大的潜力,可以产生同情的ED出院信,临床医生和患者更喜欢.
- 人工智能生成的信件可能有助于减少ED医生的工作负担.
- 需要进一步研究患者的感知和AI在临床实践中的最佳整合.
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