医疗AI的信任危机终于有了可验证的归因方案——MedFocus让医生能看清模型到底看了哪里才下诊断，做医学影像AI的团队值得试试这个因果框架。
大型视觉语言模型在医疗应用中潜力巨大，但其回答缺乏视觉证据的可信归因，引发临床信任问题。现有归因方法无法验证是否真正反映模型决策依据，因为缺乏内部推理的真实标注。研究者开发了因果评估框架，通过反事实编辑验证专家标注区域是否因果影响模型预测，并测试了11种归因方法、6个开源LVLM和两种输出模式。结果发现现有方法常无法识别模型使用的视觉证据。为此提出MedFocus，基于不平衡最优传输定位临床解剖区域，并通过定向干预测量因果效应，在空间、概念和词元层面显著优于现有方法。数据和代码已开源。
Rethinking Visual Attribution for Chest X-ray Reasoning in Large Vision Language Models
Large Vision Language Models (LVLMs) show promise in medical applications, but their inability to faithfully ground responses in visual evidence raises serious concerns about clinical trustworthiness. While visual attribution methods are widely used to explain LVLM predictions, whether these explanations actually reflect the visual evidence underlying the model's decision is largely unverified, since ground-truth annotations for internal model reasoning are typically unavailable. We address this question for chest X-ray (CXR) reasoning by developing a causal evaluation framework that retains only CXR-VQA samples for which the expert-annotated region is verified, via counterfactual editing, to be causally responsible for the model's prediction. Using this framework across 11 attribution methods, six open-source LVLMs, and two output modes (direct answer and step-by-step reasoning), we find that existing attribution methods often fail to identify the evidence used by LVLMs. To address this failure, we propose MedFocus, a concept-based attribution method that localizes clinically meaningful anatomical regions via unbalanced optimal transport and measures their causal effect on model outputs through targeted interventions. MedFocus produces spatial, concept-level, and token-level attributions and substantially outperforms prior methods, taking a step toward more trustworthy attribution for medical LVLMs. Our data and code are available at https://github.com/gzxiong/medfocus/.