MAARTA：多智能体自适应放射学教学助手
MAARTA:Multi-Agentic Adaptive Radiology Teaching Assistant
AI总结：
针对放射学学生感知错误缺乏个性化指导的问题，提出多智能体框架MAARTA，通过分析注视模式动态选择智能体并提供逐步反馈，提升诊断推理能力。
摘要：
放射学学生常因专家指导时间有限而难以发展感知专业技能，导致在视觉搜索和诊断解释中出现错误。这些感知错误，如注视遗漏、注视时间过短或误读，并未被当前的人工智能系统充分解决，这些系统侧重于诊断准确性，但未能解释错误如何发生及为何发生。为弥补这一缺口，我们引入了MAARTA（多智能体自适应放射学教学助手），一个多智能体框架，用于分析注视模式和放射学报告以提供个性化反馈。与单智能体模型不同，MAARTA根据错误复杂性动态选择智能体，实现自适应且高效的推理。通过结构化图比较专家和学生的注视行为，该系统识别遗漏的发现，并分配感知错误教师智能体来分析差异。MAARTA随后使用逐步提示帮助学生理解其错误并改进诊断推理，推进人工智能驱动的放射学教育。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
