How can LLMs improve inpatient diagnosis accuracy?
MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways
This paper introduces a Multi-Agent Inpatient Pathways (MAP) framework, using multiple LLMs acting as specialized agents (triage, diagnosis, treatment, chief) to improve the accuracy and reliability of LLM-based clinical decision support for inpatient care. It leverages a new benchmark dataset (IPDS) derived from MIMIC-IV, and shows improved performance over single LLMs, even specialized medical ones. Key multi-agent aspects include: specialized agents collaborating on a defined workflow (triage-diagnosis-treatment), structured communication between agents (context-thinking-answer), a chief agent providing supervision and guidance during training, and modules for record review, retrieval-augmented generation with a medical knowledge base, and expert guidance.