Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis
Haochun Wang, Sendong Zhao, Zewen Qiang, Nuwa Xi, Bing Qin, Ting Liu

TL;DR
This paper introduces a novel LLM-based multi-specialist consultation framework for automatic medical diagnosis, mimicking real-world collaborative diagnosis processes, and demonstrates its efficiency and improved accuracy over existing methods.
Contribution
The study proposes a tuning-free, multi-agent LLM framework that models collaborative diagnosis, reducing training requirements and incorporating implicit symptom analysis.
Findings
Outperforms baseline methods in diagnostic accuracy.
Requires less parameter updating and training time.
Provides new insights into implicit symptom roles in diagnosis.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies
