LAMA-AD: Label-Aware Multi-Agent Alzheimer's Disease Diagnosis with Counterfactual Reasoning

Chunlin Lu, Yongheng Zhang, Yao Li, Sheng Wang, Libo Qin

Abstract: The early and accurate diagnosis of Alzheimer's Disease (AD) is essential for effective intervention, yet remains challenging due to the complexity of clinical data. While large language models (LLMs) have become a promising avenue for medical diagnostics, existing approaches often overlook the implicit medical knowledge embedded in diagnostic labels, limiting diagnostic reliability and interpretability. Motivated by this, we introduce Label-Aware Multi-Agent Alzheimer's Disease Diagnosis with Counterfactual Reasoning (LAMA-AD), a labelaware multi-agent framework that explicitly incorporates clinical priors into the diagnostic reasoning process and integrates both factual and counterfactual reasoning. Specifically, LAMA-AD consists of two components: (1) Label-Aware Multi-Agent Diagnosis, which deploys multiple independent analyst agents, each guided by distinct label-aware prior knowledge, to explore diverse diagnostic hypotheses in parallel. Each agent provides supporting facts for its reasoning process, thereby enhancing both the depth and robustness of the analysis. (2) Counterfactual Reasoningsystematically assesses conflicting hypotheses to yield stable, interpretable, and highly accurate diagnostic conclusions. Experimental verification confirms LAMA-AD's superiority over established methods. Analysis further shows it generates semantically rich, logically consistent explanations.