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加强医生与患者之间的沟通,使用大型语言模型来解释病理学报告

Xiongwen Yang^1,2, Yi Xiao³, Di Liu^1,2

¹Department of Thoracic Surgery, Guizhou Provincial People's Hospital, No. 83, Zhongshan East Road, Guiyang, Guizhou, 550000, China.

BMC medical informatics and decision making

|January 24, 2025

概括

大型语言模型 (LLM) 简化了病理学报告,大大减少了医生与患者的沟通时间,并改善了患者对复杂医疗信息的理解.

科学领域:

医疗信息学医疗信息学
医疗保健中的人工智能

背景情况:

术后病理学报告包含复杂的数据,对患者来说具有挑战性.
这种复杂性可能会阻碍医生与患者之间的沟通,并影响结果.

研究的目的:

评估大型语言模型 (LLM) 在简化病理学报告中的实用性.
评估人工智能生成的解释性病理学报告 (IPR) 对患者理解和沟通效率的影响.

主要方法:

分析了来自四家医院的698份病理学报告 (2023年10月至12月).
开发基于GPT-4的解释性病理学报告 (IPR) 的模板.
评估患者的理解,使用自定义的尺度和通信时间的测量.

主要成果:

通过LLM生成的IPR显著改善了患者理解得分 (5.23至7.98,P<0.001).
医生与患者的沟通时间减少了70%以上 (35到10分钟,P<0.001).
在原始和解释性报告之间观察到高一致性 (4.95/5).

结论:

通过翻译复杂的医疗信息,LLM有效地增强了医生与患者之间的沟通.
更好的理解和更短的沟通时间显示出积极的患者参与的潜力.
人工智能有望弥合医疗保健机构的沟通差距.

关键词:

医生与病人之间的沟通.大型语言模型.手术后病理报告报告.手术瘤科的现场.

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