GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis

A new multi-agent framework called GraphDx improves diagnostic success rates from 50-68% to 79-93% while reducing test costs by 20-54%, according to a preprint on arXiv (2607.15280v1). The knowledge-enhanced system uses Large Language Models to construct Medical Diagnosis Knowledge Graphs and employs three collaborative agents (Perception, Reasoning, Decision) to balance accuracy against resource costs in sequential diagnosis. Experiments on MedQA and MIMIC-IV across DeepSeek-V3, Kimi-k2, and Llama-3.3 backbones demonstrate the framework's effectiveness for automated clinical diagnosis.

arXiv:2607.15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model LLM approaches exhibit a critical knowledge-reasoning gap: despite encoding extensive medical knowledge, they struggle to reason systematically under cost constraints, often resorting to excessive testing. We propose GraphDx, a knowledge-enhanced framework with two core innovations. First, we design an automated pipeline that leverages LLMs to construct Medical Diagnosis Knowledge Graphs MDKGs with quantized typicality, action-centric topology, and dual-objective attributes for both diagnostic relevance and cost-sensitivity. Second, we introduce three collaborative agents Perception, Reasoning, and Decision where the Perception and Decision Agents handle language understanding and generation, while the Reasoning Agent performs deterministic evidence scoring and cost-aware planning on the MDKG. Experiments on MedQA and MIMIC-IV across three LLM backbones DeepSeek-V3, Kimi-k2, Llama-3.3 show that GraphDx improves diagnostic success rates from 50--68% to 79--93% while reducing test costs by 20--54%, providing a robust, economical, and interpretable solution for automated clinical diagnosis.