  Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit
limited generality and often fall short when compared to specialized models.
Recently, LLM-based agents have been developed to address these challenges by
selecting appropriate specialized models as tools based on user inputs.
However, such advancements have not been extensively explored within the
medical domain. To bridge this gap, this paper introduces the first agent
explicitly designed for the medical field, named \textbf{M}ulti-modal
\textbf{Med}ical \textbf{Agent} (MMedAgent). We curate an instruction-tuning
dataset comprising six medical tools solving seven tasks, enabling the agent to
choose the most suitable tools for a given task. Comprehensive experiments
demonstrate that MMedAgent achieves superior performance across a variety of
medical tasks compared to state-of-the-art open-source methods and even the
closed-source model, GPT-4o. Furthermore, MMedAgent exhibits efficiency in
updating and integrating new medical tools.
