用多智能体分析看片习惯，帮放射科学生发现看漏的细节
MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant
- 根据错误复杂度动态选智能体，分步指导看片问题
- 对比专家与学生眼动轨迹，定位漏诊原因
- 适合需要提升影像判读能力的医学生和教学者
放射科学生因缺乏专家指导，常在视觉搜索和诊断解读中出现错误，如遗漏注视点、停留时间过短或误判。现有AI系统仅关注诊断准确率，无法解释错误成因。为此，我们提出MAARTA（多智能体自适应放射科教学助手），通过分析眼动模式与放射报告，提供个性化反馈。该框架基于错误复杂度动态选择智能体，利用结构化图对比专家与学生的注视行为，识别漏诊项，并由感知错误教师智能体分析差异。系统采用逐步提示法帮助学生理解错误原因，提升诊断推理能力，推动人工智能在放射科教育中的应用。
原文摘要 · Abstract (English)
Radiology students often struggle to develop perceptual expertise due to limited expert mentorship time, leading to errors in visual search and diagnostic interpretation. These perceptual errors, such as missed fixations, short dwell times, or misinterpretations, are not adequately addressed by current AI systems, which focus on diagnostic accuracy but fail to explain how and why errors occur. To address this gap, we introduce MAARTA (Multi-Agentic Adaptive Radiology Teaching Assistant), a multi-agent framework that analyzes gaze patterns and radiology reports to provide personalized feedback. Unlike single-agent models, MAARTA dynamically selects agents based on error complexity, enabling adaptive and efficient reasoning. By comparing expert and student gaze behavior through structured graphs, the system identifies missed findings and assigns Perceptual Error Teacher agents to analyze discrepancies. MAARTA then uses step-by-step prompting to help students understand their errors and improve diagnostic reasoning, advancing AI-driven radiology education.
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