科技情报研究2026，Vol.8Issue(2)：140-151,12.DOI:10.19809/j.cnki.kjqbyj.2026.02.013
基于知识增强的医疗健康问答系统研究综述
A Survey on Knowledge-Enhanced Healthcare Question Answering Systems
摘要
Abstract
[Purpose/significance]This paper aims to review the research progress and applications of knowledge enhancement techniques in healthcare question answering systems,in response to the limitations of traditional systems in knowledge representation and reasoning,as well as challenges faced by current large language model-based systems,such as insufficient domain knowledge,privacy concerns,and hallucination.The review provides a systematic reference for improving the precision and knowledge reliability of such systems.[Process/method]Focusing on knowledge enhancement strategies,this paper firstly outlines their fundamental concepts and overall framework.The strategies are then categorized into explicit and implicit types,with an analysis of their characteristics,implementation methods,and applications in typical healthcare scenarios.Finally,future research directions are discussed.[Result/conclusion]The study indicates that knowledge enhancement techniques,which integrate external medical knowledge during the pre-training or inference stages of large lan-guage models,can effectively improve the accuracy,interpretability,and trustworthiness of the models.Explicit enhancement strategies emphasize the traceability and structured integration of knowledge,while implicit strategies focus on the semantic internalization and gen-erative flexibility of knowledge.Their synergistic application provides crucial support for building more intelligent and reliable healthcare question answering systems.关键词
知识增强/问答系统/大语言模型/医学知识库/医疗健康Key words
knowledge enhancement/question answering systems/large language models/medical knowledge bases/healthcare分类
社会科学引用本文复制引用
苏俊楠,韩普,魏建香..基于知识增强的医疗健康问答系统研究综述[J].科技情报研究,2026,8(2):140-151,12.基金项目
国家社会科学基金项目"面向多模态医疗健康数据的知识组织模式研究"(编号:22BTQ096) （编号:22BTQ096）
江苏省高校哲学社会科学创新团队(数智驱动的健康大数据管理创新团队)建设项目 （数智驱动的健康大数据管理创新团队）
国家级大学生创新训练计划项目"大语言模型融入外部知识的医疗问答系统研究"(编号:202410293066Z). （编号:202410293066Z）
