基于LLM的临床史记录系统:以人为本的方法
Dasol Choi1, Yujoon Jung2, Joonyoung Kim3
1Yonsei University.
Studies in health technology and informatics
|August 8, 2025
概括
我们使用GPT-4o创建了一种新方法,用于生成标准化患者 (SP) 病例,用于医学教育. 这些人工智能产生的病例显示出良好的临床真实性和准确性,为传统培训方法提供了有价值的补充.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 临床技能培训 临床技能培训
背景情况:
- 标准化患者 (SP) 对于医疗培训至关重要,但开发和管理需要大量资源.
- 传统的SP程序面临与成本,可扩展性和一致性相关的挑战.
- 需要创新,高效的方法来创建高质量的临床培训材料.
研究的目的:
- 开发和验证一种系统的方法,用于使用GPT-4o生成标准化的患者病例.
- 评估人工智能产生的SP病例的临床真实性和准确性.
- 评估虚拟患者互动的有效性和学生对虚拟患者互动的看法.
主要方法:
- 使用GPT-4o开发了一个系统的病例生成协议.
- 在三个不同的首席投诉中创建了12个SP案件.
- 专家教师验证了病例的临床真实性和准确性.
- 医学学生 (n=15) 与虚拟患者互动并提供反.
主要成果:
- 专家验证表明了良好的临床真实性 (信息流:3.7±0.5,现实性:3.4±0.8) 和准确性 (3.4±0.5).
- 医学学生对虚拟患者互动的评价是积极的,在适当性 (7.9/10) 和准确性 (7.5/10) 上得分很高.
- 该研究表明,使用LLM来创建有效的SP培训案例的可行性.
结论:
- 像GPT-4o这样的大型语言模型可以系统地生成标准化的患者病例.
- 人工智能产生的SP病例显示出有希望的临床真实性和教育目的的准确性.
- 这种方法为医学教育中的传统标准化患者计划提供了可行的,资源高效的补充.
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