A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
Journal:
arXiv
Published Date:
Feb 16, 2025
Abstract
Large Language Models (LLMs) are transforming healthcare through the
development of LLM-based agents that can understand, reason about, and assist
with medical tasks. This survey provides a comprehensive review of LLM-based
agents in medicine, examining their architectures, applications, and
challenges. We analyze the key components of medical agent systems, including
system profiles, clinical planning mechanisms, medical reasoning frameworks,
and external capacity enhancement. The survey covers major application
scenarios such as clinical decision support, medical documentation, training
simulations, and healthcare service optimization. We discuss evaluation
frameworks and metrics used to assess these agents' performance in healthcare
settings. While LLM-based agents show promise in enhancing healthcare delivery,
several challenges remain, including hallucination management, multimodal
integration, implementation barriers, and ethical considerations. The survey
concludes by highlighting future research directions, including advances in
medical reasoning inspired by recent developments in LLM architectures,
integration with physical systems, and improvements in training simulations.
This work provides researchers and practitioners with a structured overview of
the current state and future prospects of LLM-based agents in medicine.
