通过简化放射学报告在瘤学中提高医生与患者的沟通使用GPT-4:多中心定量研究
Xiongwen Yang1,2, Yi Xiao3, Di Liu1,2
1Department of Thoracic Surgery, Guizhou Provincial People's Hospital, Guiyang, China.
Journal of medical Internet research
|April 17, 2025
概括
像GPT-4这样的大型语言模型可以简化复杂的瘤放射学报告,提高患者的理解和沟通效率. 这种人工智能应用程序增强了医生和患者之间的互动,并可能改善健康结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 医生与患者之间的沟通在瘤学中至关重要,但放射学报告中含有技术术语,阻碍了患者的理解.
- 这种复杂性影响了患者的参与,决策和整体护理.
- 大型语言模型 (LLM) 为简化医疗信息提供了一个潜在的解决方案.
研究的目的:
- 评估使用GPT-4来简化瘤放射学报告的可行性和有效性.
- 通过更清晰的报告解释,增强医生与患者之间的沟通.
主要方法:
- 进行了对698份恶性瘤放射学报告的回顾性分析.
- GPT-4从70个选定的报告中生成简化解释性放射学报告 (IRR).
- 放射科医生验证了报告的一致性,而非专业志愿者和医生评估了可读性和通信效率.
主要成果:
- 简化的IRR增加了字数,但被志愿者阅读的速度更快,更容易.
- 在阅读简化报告后,患者的理解得分显著提高.
- 医生与患者的沟通时间大幅减少,表明效率提高.
结论:
- 在简化瘤放射学报告方面,LLM,特别是GPT-4具有显著的潜力.
- 简化报告提高了患者的理解,并简化了医生与患者的互动.
- 这样的AI应用程序可以增强医疗保健沟通和患者的结果.
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