从使用Gen-AI到个性化药物说明书的初步结果
Joao Almeida1,2, Catherine Chronaki1, Anne Moen3
1HL7 Europe, Brussels, Belgium.
Studies in health technology and informatics
|August 8, 2025
概括
大型语言模型 (LLM) 可以从健康记录中创建个性化的患者信息传单 (PIL) 摘要. 需要进一步的研究来提高准确性和个性化,以便更好地遵守药物治疗.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 传统的患者信息传单 (PIL) 是静态的,通常难以理解.
- 欧洲药物管理局 (EMA) 正在使用HL7 FHIR®标准转向电子产品信息 (ePI).
- 个性化医疗需要量身定制的患者信息,以提高理解和遵守.
研究的目的:
- 探索大型语言模型 (LLM) 的潜力,以生成个性化的PIL摘要.
- 评估使用国际患者摘要 (IPS) 数据来定制药物信息的可行性.
- 通过简化,个性化的PIL内容来增强患者的理解和坚持.
主要方法:
- 使用三个开源LLM进行实验,以生成PIL摘要.
- 使用带有和没有个性化的提示,以IPS数据为指导.
- 评估LLM生成的摘要的连贯性,准确性和个性化.
主要成果:
- LLM 证明了能够生成 PIL 的连贯摘要的能力.
- 目前的LLM输出需要提高准确性和个性化.
- 个性化的PIL显示出增强患者与医生的沟通和共享决策的潜力.
结论:
- LLM提供了一个有前途的途径,用于创建个性化的患者信息.
- 进一步开发快速工程和模型微调对于临床应用至关重要.
- 个性化的PIL可以支持共享决策,并改善患者对治疗的参与度.
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