GraphDx: A Cost-Aware Multi-Agent Framework for Sequential Diagnosis
GraphDx is a knowledge-enhanced multi-agent framework designed for sequential medical diagnosis that aims to balance diagnostic accuracy with resource costs. It leverages large language models to construct Medical Diagnosis Knowledge Graphs and employs three collaborative agents—Perception, Reasoning, and Decision—for systematic information gathering and decision-making. Experiments on MedQA and MIMIC-IV datasets demonstrate that GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%.
Why it matters: This framework offers a potentially more cost-effective and interpretable approach to automated clinical diagnosis by integrating LLM knowledge with structured, cost-aware reasoning.
Full story at: arXiv AI/ML ↗