# ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agents

arXiv: https://arxiv.org/abs/2410.17657 · HTML: https://mlsanity.com/paper/2410.17657/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://mlsanity.com/paper/2404.06395/) (2024)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale](https://mlsanity.com/paper/2406.19280/) (2024)
- [MMedAgent: Learning to Use Medical Tools with Multi-modal Agent](https://mlsanity.com/paper/2407.02483/) (2024)
- [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Towards Conversational Diagnostic AI](https://mlsanity.com/paper/2401.05654/) (2024)
- [EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records](https://mlsanity.com/paper/2401.07128/) (2024)
- [MedCalc-Bench: Evaluating Large Language Models for Medical Calculations](https://mlsanity.com/paper/2406.12036/) (2024)
- [Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator](https://mlsanity.com/paper/2403.08495/) (2024)
- [LongHealth: A Question Answering Benchmark with Long Clinical Documents](https://mlsanity.com/paper/2401.14490/) (2024)
- [AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning](https://mlsanity.com/paper/2402.13225/) (2024)
- [ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning](https://mlsanity.com/paper/2404.14777/) (2024)
- [Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation](https://mlsanity.com/paper/2405.18346/) (2024)
- [Hallucination Benchmark in Medical Visual Question Answering](https://mlsanity.com/paper/2401.05827/) (2024)
- [Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients](https://mlsanity.com/paper/2404.05144/) (2024)
- [BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science](https://mlsanity.com/paper/2407.00466/) (2024)

## Built on by (most cited first, 20 of 20)

- [MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science](https://mlsanity.com/paper/2506.04405/) (2025)
- [EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis](https://mlsanity.com/paper/2510.25628/) (2025)
- [Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications](https://mlsanity.com/paper/2501.02460/) (2025)
- [WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning](https://mlsanity.com/paper/2505.22942/) (2025)
- [HealthAgentBench: A Unified Benchmark Suite of Realistic Agentic Healthcare Environments for Challenging Frontier AI Agents](https://mlsanity.com/paper/2606.31179/) (2026)
- [LongMedBench: Benchmarking Medical Agents for Long-Horizon Clinical Decision-Making](https://mlsanity.com/paper/2607.09322/) (2026)
- [AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization](https://mlsanity.com/paper/2601.13918/) (2026)
- [Agent Memory Distillation: Empowering Small LLM Agents with Hierarchical Teacher Memory](https://mlsanity.com/paper/2608.07169/) (2026)
- [MediSkill-Evo: Process-Constrained Self-Evolution for Evidence-Grounded Clinical Interaction](https://mlsanity.com/paper/2608.23397/) (2026)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [Infherno: End-to-end Agent-based FHIR Resource Synthesis from Free-form Clinical Notes](https://mlsanity.com/paper/2507.12261/) (2025)
- [An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance](https://mlsanity.com/paper/2605.02709/) (2026)
- [Configurable Clinical Information Extraction with Agentic RAG: What Works, What Breaks, and Why](https://mlsanity.com/paper/2606.19602/) (2026)
- [PULSE: Agentic Investigation with Passive Sensing for Proactive Affective Intervention in Cancer Survivorship](https://mlsanity.com/paper/2605.17679/) (2026)
- [CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation](https://mlsanity.com/paper/2606.01094/) (2026)
- [Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation](https://mlsanity.com/paper/2607.17341/) (2026)
- [Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance](https://mlsanity.com/paper/2608.03403/) (2026)
- [Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis](https://mlsanity.com/paper/2609.24620/) (2026)
- [Improving Medical Calculation of LLMs with Embedded Coding](https://mlsanity.com/paper/2609.31908/) (2026)

## Abstract (arXiv)

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 tool-wise 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 long-term 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 ClinicalAgent Benchmark 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.