Causal-Enhanced AI Agents for Medical Research Screening

TL;DR

This paper introduces a causal graph-enhanced AI system for medical research screening that improves accuracy, reduces hallucinations, and enhances interpretability in evidence synthesis tasks.

Contribution

It presents a novel causal reasoning framework with knowledge graphs that significantly improves trustworthiness and accuracy in medical literature review AI systems.

Findings

Achieved 95% accuracy and 100% retrieval success on dementia abstracts.

Zero hallucinations in the system compared to 34% in baseline models.

Demonstrated transferable principles for trustworthy medical AI.

Abstract

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.