Med-StepBench：一种用于评估医学视觉-语言模型幻觉的分层推理框架
Med-StepBench: A Hierarchical Reasoning Framework for Evaluating Hallucinations in Medical Vision-Language Models
AI总结：
本文提出Med-StepBench，首个针对3D肿瘤PET/CT图像的分步幻觉检测基准，通过12000张图像和100万对图像-陈述数据，揭示了现有VLMs在多步临床推理中的系统性缺陷。
摘要：
大型视觉-语言模型（VLMs）在医学图像理解中表现出色，但常生成临床合理却错误的陈述，引发重大安全问题。现有医学幻觉基准主要关注2D影像的一次性诊断问题，无法深入评估预测是否基于正确的定位和异常识别，导致关键推理错误被看似正确的诊断掩盖。我们引入Med-StepBench，首个大规模的3D肿瘤PET/CT分步幻觉检测基准，包含超过12,000张图像和100万张图像-陈述对，涵盖体积分层和多视图2D数据。该基准将临床推理分解为四个专家设计的诊断阶段。通过临床验证的标注，我们首次对通用和医学VLMs进行分步评估，揭示了被聚合准确度指标掩盖的系统性失败模式。此外，我们证明当前VLMs对具有对抗性但临床合理的中间解释高度敏感，尽管存在矛盾的视觉证据，这些解释会显著放大幻觉。综上，我们的发现突显了多步临床推理基础的局限性，并确立Med-StepBench作为开发更安全可靠医学VLMs的严格基准。
英文摘要：
Large vision-language models (VLMs) demonstrate strong performance in medical image understanding, but frequently generate clinically plausible yet incorrect statements, raising significant safety concerns. Existing medical hallucination benchmarks primarily focus on 2D imaging with one-shot diagnostic questions, offering limited insight into whether predictions are grounded in correct localization and abnormality identification, allowing critical reasoning errors to remain hidden behind seemingly correct diagnoses. We introduce Med-StepBench, the first large-scale benchmark for step-wise hallucination detection in 3D oncological PET/CT, comprising over 12,000 images and more than 1,000,000 image-statement pairs across volumetric and multi-view 2D data, which decomposes clinical reasoning into four expert-designed diagnostic stages. Using clinician-verified annotations, we perform the first step-level evaluation of general-purpose and medical VLMs, revealing systematic failure modes obscured by aggregate accuracy metrics. Furthermore, we show that current VLMs are highly susceptible to adversarial yet clinically plausible intermediate explanations, which significantly amplify hallucinations despite contradictory visual evidence. Together, our findings highlight fundamental limitations in grounding multi-step clinical reasoning and establish Med-StepBench as a rigorous benchmark for developing safer and more reliable medical VLMs.
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。
