The RadA-BenchPlat platform rigorously evaluates how effectively modern LLMs can serve as the core of autonomous radiology agents in clinical settings.

• Tests LLMs using 2,200 radiologist-verified synthetic patient records across six anatomical regions and five imaging modalities
• Evaluates 24,200 question-answer pairs simulating diverse clinical scenarios
• Defines ten categories of specialized tools for agent-driven radiology task solving
• Assesses seven leading LLM models for radiology agent applications

This research provides critical insights for healthcare organizations seeking to integrate AI-powered radiology assistants into clinical workflows, potentially improving diagnostic accuracy, efficiency, and patient care.

How Well Can Modern LLMs Act as Agent Cores in Radiology Environments?