MEDIC-AD：迈向医疗视觉-语言模型的临床智能
MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence
AI总结：
MEDIC-AD通过分阶段框架提升医疗视觉-语言模型在病变检测、症状跟踪和视觉可解释性方面的性能，实现临床应用中的最优表现。
摘要：
病变检测、症状跟踪和视觉可解释性是实际医疗图像分析中的核心要素，然而当前的医疗视觉-语言模型（VLMs）仍缺乏将广泛知识转化为临床可操作输出的机制。为弥合这一差距，我们提出了MEDIC-AD，一个面向临床的VLM，通过分阶段框架强化这三项能力。首先，可学习的异常感知标记（<Ano>）促使模型关注异常区域并构建更具判别性的病变中心表示。其次，图像间差异标记（<Diff>）显式编码不同研究之间的时间变化，使模型能够区分疾病负担的恶化、改善和稳定。最后，专门的可解释性阶段训练模型生成热图，突出与病变相关的区域，提供与模型推理一致的清晰视觉证据。通过我们的分阶段设计，MEDIC-AD在异常检测、症状跟踪和异常分割方面稳步提升性能，相较于闭源和医学专用基线，实现了最先进的结果。在真实纵向临床数据上进一步评估表明，MEDIC-AD在实际患者监测和决策支持工作流程中提供了稳定的预测和临床忠实的解释。
英文摘要：
Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs. To bridge this gap, we present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework. First, learnable anomaly-aware tokens (<Ano>) encourage the model to focus on abnormal regions and build more discriminative lesion centered representations. Second, inter image difference tokens (<Diff>) explicitly encode temporal changes between studies, allowing the model to distinguish worsening, improvement, and stability in disease burden. Finally, a dedicated explainability stage trains the model to generate heatmaps that highlight lesion-related regions, offering clear visual evidence that is consistent with the model's reasoning. Through our staged design, MEDIC-AD steadily boosts performance across anomaly detection, symptom tracking, and anomaly segmentation, achieving state-of-the-art results compared with both closed source and medical-specialized baselines. Evaluations on real longitudinal clinical data collected from real hospital workflows further show that MEDIC-AD delivers stable predictions and clinically faithful explanations in practical patient-monitoring and decision-support workflows
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。
