通过检索增强生成来使用药物的强化LLM支持的指令
Davi Dos Reis de Jesus1, Antônio Pereira de Souza Júnior1, Elisa Tuler de Albergaria1
1Department of Computer Science, Universidade Federal de São João Del Rei, Praça Frei Orlando, 170, Centro, São João del-Rei, Minas Gerais, 36307-352, Brazil.
Computers in biology and medicine
|October 12, 2025
概括
使用大语言模型 (LLM) 的检索增强生成 (RAG) 显著改善了患者的药物说明. 这种人工智能方法提高了清晰度和准确性,通过整合权威信息来促进更安全的药物使用.
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
- 患者沟通 患者沟通
背景情况:
- 目前的处方系统在个性化患者指令以提高清晰度方面面临着挑战.
- 大型语言模型 (LLM) 显示了改善医疗保健沟通的潜力.
研究的目的:
- 为了研究LLM检索增强生成 (RAG) 的有效性,使用患者信息传单 (PIL) 来生成更清晰的药物说明.
- 为了评估LLM生成的患者指令的不同提示策略.
主要方法:
- 利用119个门诊病例的数据库来进行LLM输入.
- 评估的开源Llama3具有标准,增强和RAG增强的提示,包含巴西监管PIL数据.
- 评估了五名医生生成的指令的充分性,清晰性,个性化和质量;计算了等号相似性.
主要成果:
- RAG显著提高了指令的充分性 (p=0.001) 和清晰度 (p=0.012).
- 使用RAG的增强提示减少了关键错误,例如错误或不完整的说明和事实上的不准确性.
- 快速工程尽量减少模糊和未经请求的指令,与最小的幻觉.
结论:
- 人工智能,特别是具有权威PIL数据的RAG,可以提高药物安全性和患者友好的沟通.
- 人类验证对于确保无错且安全地实施人工智能生成的医疗信息至关重要.
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