整合医学AI的LLM和知识图:进步,挑战和未来方向
IEEE journal of biomedical and health informatics
|October 16, 2025
概括
将大型语言模型 (LLM) 与知识图 (KG) 集成,可以通过提高数据可靠性和推理来增强医疗AI. 这种协同作用促进了临床决策支持和知识发现,解决了现实世界采用的关键挑战.
科学领域:
- 人工智能在医学中的应用
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 大型语言模型 (LLM) 提供先进的自然语言理解,但缺乏结构化的事实知识.
- 知识图 (KG) 为医疗保健等高风险领域提供可靠,结构化的数据.
- 整合LLM和KG对于推进医疗AI能力至关重要.
研究的目的:
- 综合LLMs和KG在医疗AI中的整合.
- 探索LLM-KG协同效应的方法,应用和评估.
- 确定AI在医疗保健中的挑战和未来方向.
主要方法:
- 审查最近在LLM-KG整合方法的进展.
- 分析了三个关键的整合框架:KG增强的LLMs,LLM增强的KG和协同系统.
- 检查知识提取,临床决策支持和可解释性方面的应用.
主要成果:
- 法学士-KG协同作用显著增强了医疗人工智能应用,包括诊断和个性化治疗.
- 整合框架改善了知识提取,临床决策支持和模型可解释性.
- 关键的挑战包括数据异质性,透明度,可扩展性和道德考虑.
结论:
- 法学士-KG集成为医疗AI提供了巨大的好处,但需要解决临床采用的关键挑战.
- 未来的方向包括跨领域集成,神经符号AI,因果推理和多代理系统.
- 可扩展性,实时更新和隐私对于医疗领域负责任的AI部署至关重要.
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