研究进展和大语言模型在中国医疗保健领域的共享决策中的应用影响
Xuejing Li1,2,3, Sihan Chen1,2,3, Meiqi Meng1,2,3
1School of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Frontiers in public health
|July 25, 2025
概括
在中国,共享决策 (SDM) 面临挑战. 大型语言模型 (LLM) 可以改善患者参与和决策质量,但需要进一步开发以适应文化和复杂的医疗场景.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的人工智能
背景情况:
- 共享决策 (SDM) 是一种以患者为中心的模式.
- 中国的SDM实施受到资源差异,知识差距和文化因素的挑战.
- 现有的障碍包括时间限制,患者参与,缺乏工具和报销问题.
研究的目的:
- 探索中国的SDM挑战.
- 研究大型语言模型 (LLM) 应对这些挑战的潜力.
- 确定在中国SDM中LLM应用的未来方向.
主要方法:
- 分析目前在中国实施SDM的障碍.
- 在医疗保健自然语言处理的LLM能力的评估.
- 在中国医疗保健的背景下,识别LLM的优势和局限性.
主要成果:
- 法律法学在沟通效率,个性化决策支持和多方合作方面具有优势.
- 像信息整合和情绪分析这样的LLM功能增强了患者的参与度.
- 局限性包括本地化,处理复杂的医疗链和整合传统中医 (TCM).
结论:
- 通过提高患者参与度和决策质量,LLM显示出推动中国SDM的前景.
- 需要进一步的研究来开发具有文化适应性,能够处理复杂,多方面的医疗保健场景的LLM.
- 未来的工作应侧重于综合知识图和改进的模型可解释性,以便有效地将LLM整合到中国SDM中.
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