通过专家之眼：一个基于放射学家目光和推理训练的基础视觉语言模型
Seeing Through Experts Eyes A Foundational Vision Language Model Trained on Radiologists Gaze and Reasoning
AI总结：
本文提出GazeX模型，通过放射学家的眼动数据模拟专家推理过程，提升医学影像分析的准确性和可解释性。
摘要：
大规模视觉语言模型在自动化胸部X光解读中展现出潜力，但其临床应用受限于模型输出与放射学家推理之间的差距。大多数系统优化语义信息而不模仿专家视觉检查方式，常忽略关键发现或偏离诊断流程。放射学家遵循结构化协议（如ABCDEF方法）以确保所有临床相关区域被系统检查，减少遗漏并支持可靠推理。我们引入GazeX，一个利用放射学家眼动数据作为行为先验的视觉语言模型。通过在预训练中整合眼动轨迹和注视模式，GazeX学习跟随放射学家注意力的空间和时间结构，并以临床有意义的顺序整合观察。使用超过30,000个关键帧数据集，我们证明GazeX在放射科报告生成、疾病定位和视觉问答中产生更准确、可解释且专家一致的输出，利用231,835例放射学研究、780,014个问答对和1,162个带边界框的图像句子对。不同于自主报告系统，GazeX生成可验证的证据人工制品，包括检查轨迹和与局部区域关联的发现，使高效的人类验证和安全的人机协作成为可能。通过专家之眼学习为更可信、可解释且诊断稳健的AI系统在放射学及其他领域提供了实用路径。
英文摘要：
Large scale vision language models have shown promise in automating chest Xray interpretation, yet their clinical utility remains limited by a gap between model outputs and radiologist reasoning. Most systems optimize for semantic information without emulating how experts visually examine medical images, often overlooking critical findings or diverging from established diagnostic workflows. Radiologists follow structured protocols (e.g., the ABCDEF approach) that ensure all clinically relevant regions are systematically examined, reducing missed findings and supporting reliable diagnostic reasoning. We introduce GazeX, a vision language model that leverages radiologists' eye tracking data as a behavioral prior to model expert diagnostic reasoning. By incorporating gaze trajectories and fixation patterns into pretraining, GazeX learns to follow the spatial and temporal structure of radiologist attention and integrates observations in a clinically meaningful sequence. Using a curated dataset of over 30,000 gaze key frames from five radiologists, we demonstrate that GazeX produces more accurate, interpretable, and expert consistent outputs across radiology report generation, disease grounding, and visual question answering, utilizing 231,835 radiographic studies, 780,014 question answer pairs, and 1,162 image sentence pairs with bounding boxes. Unlike autonomous reporting systems, GazeX produces verifiable evidence artifacts, including inspection trajectories and finding linked localized regions, enabling efficient human verification and safe human AI collaboration. Learning through expert eyes provides a practical route toward more trustworthy, explainable, and diagnostically robust AI systems for radiology and beyond.
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。
