New CXRAgent uses LLMs for multi-stage chest X-ray interpretation

Researchers have developed CXRAgent, a novel multi-stage agent designed to improve chest X-ray interpretation using Large Language Models (LLMs). This agent features a central director that orchestrates tool invocation, diagnostic planning, and collaborative decision-making. CXRAgent aims to enhance adaptability and credibility by validating tool outputs with visual evidence and assembling expert teams for complex reasoning tasks. Experiments demonstrate its strong performance and generalization capabilities across various complexity levels of clinical tasks. AI

IMPACT This agent could improve diagnostic accuracy and efficiency in medical imaging by leveraging LLMs for complex reasoning.

RANK_REASON The cluster contains a research paper detailing a new AI agent for a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• chest radiograph
• CXRAgent
• DagsHub
• Evidence-driven Validator
• Hugging Face
• Jinhui Lou

• paper
• model release

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