Automatic diagnosis is a significant application of AI in healthcare, where
diagnoses are generated based on the symptom description of patients. Previous
works have approached this task directly by modeling the relationship between
the normalized symptoms and all possible diseases. However, in the clinical
diagnostic process, patients are initially consulted by a general practitioner
and, if necessary, referred to specialists in specific domains for a more
comprehensive evaluation. The final diagnosis often emerges from a
collaborative consultation among medical specialist groups. Recently, large
language models have shown impressive capabilities in natural language
understanding. In this study, we adopt tuning-free LLM-based agents as medical
practitioners and propose the Agent-derived Multi-Specialist Consultation
(AMSC) framework to model the diagnosis process in the real world by adaptively
fusing probability distributions of agents over potential diseases.
Experimental results demonstrate the superiority of our approach compared with
baselines. Notably, our approach requires significantly less parameter updating
and training time, enhancing efficiency and practical utility. Furthermore, we
delve into a novel perspective on the role of implicit symptoms within the
context of automatic diagnosis.

采用调谐自由的 LLM（大语言模型）代理作为医学从业者，我们提出了基于代理的多专科咨询（AMSC）框架，通过自适应地融合代理对潜在疾病的概率分布来模拟真实世界中的诊断过程。实验结果显示了我们的方法相比基线的优越性。值得注意的是，我们的方法需要极少的参数更新和训练时间，提高了效率和实用性。此外，我们深入探讨了在自动诊断背景下的隐性症状的新角度。