Year: 2025
Type: conference-paper
Abstract: Large Language Models (LLMs) have shown promising potential in the medical domain, assisting with tasks like clinical note generation and patient communication.However, current LLMs are limited to text-based communication, hindering their ability to interact with diverse forms of information in clinical environments.Despite clinical agents succeeding in diverse signal interaction, they are oriented to a single clinical scenario and hence fail for broader applications.To evaluate clinical agents holistically, we propose ClinicalAgent Bench (CAB), a comprehensive medical agent benchmark consisting of 18 tasks across five key realistic clinical dimensions.Building on this, we introduce REFLECTOOL, a novel framework that excels at utilizing domain-specific tools within two stages.The first optimization stage progressively enlarges a long-term memory by saving successful solving processes and toolwise experience of agents in a tiny pre-defined training set.In the following inference stage, REFLECTOOL can search for supportive successful demonstrations from already built longterm memory to guide the tool selection strategy, and a verifier improves the tool usage according to the tool-wise experience with two verification methods-iterative refinement and candidate selection.Extensive experiments on CAB demonstrate that REFLECTOOL surpasses the pure LLMs with more than 10 points and the well-established agent-based methods with 3 points, highlighting its adaptability and effectiveness in solving complex clinical tasks.Our code and datasets are available at https: //github.com/BlueZeros/ReflecTool.
Authors: Yusheng Liao, Shuyang Jiang, Yanfeng Wang, Yu Wang
Institutions: Fudan University, Shanghai Artificial Intelligence Laboratory
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Award: G6360183275
OpenAlex ID: W4412945250