使用openEHR和大型语言模型进行结构化数据的相互作用,用于前列腺癌的临床决策支持
Philippe Kaiser1, Shan Yang2, Michael Bach2
1Institute of Radiology and Nuclear Medicine, University Hospital Basel, Petersgraben 4, Basel, 4031, Switzerland. Philippejulien.kaiser@usb.ch.
World journal of urology
|January 13, 2025
概括
大型语言模型 (LLM) 在推前列腺癌治疗方面表现出很高的准确性,与多学科团队的决定相匹配. 这种人工智能应用程序可以简化癌症护理工作流程并降低成本.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 多学科团队 (MDT) 对于癌症治疗至关重要,但资源密集.
- 在MDTs中的临床决策有助于医疗保健成本.
- 大型语言模型 (LLM) 具有提高临床决策效率和降低成本的潜力.
研究的目的:
- 评估LLM在生成前列腺癌治疗建议中的准确性.
- 将LLM产生的建议与已建立的多学科团队 (MDT) 决策进行比较.
- 探索LLMs在优化癌症护理工作流程和成本方面的潜力.
主要方法:
- 对171名新诊断的前列腺癌患者进行了回顾性研究.
- 两个LLM (ChatGPT-4,Claude-3-Opus) 提供了结构化的临床数据和欧洲泌尿病学会 (EAU) 的指导方针.
- 将LLM建议与实际的MDT会议建议进行了比较.
主要成果:
- 终身治疗师表明93%的患者遵守了MDT治疗建议.
- 差异发生在9%的病例中,通常是由于缺乏临床信息.
- 在3%的案例中,尽管数据完整,但LLM的建议偏离了EAU的指导方针.
结论:
- LLM可以为前列腺癌患者提供准确的治疗建议.
- 法律法学有潜力简化MDT流程,使专家能够专注于复杂的案例.
- 在瘤学中,人工智能辅助的决策可能会提高效率并降低医疗保健支出.
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