How can LLMs improve medical image analysis?
MedRAX: Medical Reasoning Agent for Chest X-ray
This paper introduces MedRAX, a specialized AI agent for interpreting chest X-rays (CXRs). It integrates various CXR analysis tools (e.g., segmentation, classification, report generation) with a large language model (LLM) within a ReAct (Reasoning and Acting) loop to answer complex medical queries. A new benchmark, ChestAgentBench, is also introduced for evaluating multi-step reasoning in CXR interpretation. Key points for LLM-based multi-agent systems include: specialized tool integration without retraining, dynamic tool orchestration for complex reasoning, improved performance over end-to-end and specialized models, and the potential of hybrid architectures combining LLMs with specialized tools. MedRAX offers a structured approach, improving transparency and reliability compared to relying solely on LLMs.