因果增强的医疗研究筛查AI代理
Causal-Enhanced AI Agents for Medical Research Screening
AI总结：
本文提出一种因果图增强的AI代理，用于医疗研究筛选，通过显式因果推理和双层知识图谱提升系统综述的准确性和可解释性。
摘要：
系统综述对于循证医学至关重要，但手动审查超过150万篇年度出版物是不可行的。当前的AI方法在系统综述任务中存在幻觉问题，有研究报告的幻觉率从早期模型的28-40%到现代实现的2-15%不等，当错误影响患者护理时这是不可接受的。我们提出了一种结合显式因果推理和双层知识图谱的因果图增强检索增强生成系统。我们的方法强制执行证据优先的协议，其中每个因果主张都追溯到检索到的文献，并自动生成有向无环图来可视化干预-结果路径。对234个痴呆症锻炼摘要的评估显示，CausalAgent实现了95%的准确率、100%的检索成功率和零幻觉，而基线AI的准确率为34%且幻觉率为10%。自动因果图能够实现显式机制建模、视觉合成和增强的可解释性。虽然这个概念验证评估使用了十个专注于痴呆症锻炼研究的问题，但该架构方法展示了可转移的原则，适用于可信的医学AI以及因果推理在高风险医疗保健中的潜力。
英文摘要：
Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
