发表机构
Purdue University; Indiana University School of Medicine（普渡大学; 印第安纳大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对炎症性肠病影像评估中视觉语言模型推理不足和可解释性差的问题，提出ARISE智能体框架，通过五阶段透明工作流提升诊断性能并揭示推理过程。
AI 中文摘要
炎症性肠病（IBD）需要频繁的基于影像的评估，然而对无线胶囊内镜（WCE）和肠道超声等模态的解读仍高度依赖专家经验。视觉语言模型（VLM）在多模态医学图像分析中展现出巨大潜力，但其临床应用受到领域特定推理能力不足、易产生幻觉、细粒度诊断中高质量训练数据稀缺以及可解释性有限的阻碍。我们提出ARISE（基于图像接地自评估的自适应智能体推理），一个将少样本医学图像理解建模为顺序智能体工作流的自主规划框架。ARISE将智能体执行组织为透明的5阶段工作流：假设生成、图像接地证据总结、证据条件化细化、符号验证和最终诊断。我们将ARISE应用于两个独立患者队列的IBD评估：用于克罗恩病的无线胶囊内镜（WCE）图像和用于溃疡性结肠炎的B型超声数据。ARISE持续优于基线VLM的诊断性能，同时揭示推理成功或失败的环节，为临床决策支持和实际部署提供了更可解释的基础。
英文摘要
Inflammatory bowel disease (IBD) requires frequent imaging-based assessment, yet interpretation of modalities such as wireless capsule endoscopy (WCE) and intestinal ultrasound remains heavily dependent on specialist expertise. Vision-Language Models (VLMs) demonstrate significant potential in multimodal medical image analysis, but their clinical adoption is hindered by their insufficient domain-specific reasoning, susceptibility to hallucination, scarcity of high quality training data in fine-grained diagnostics and limited interpretability. We introduce ARISE (Adaptive Agentic Reasoning with Image-grounded Self-Evaluation), an autonomous planning framework that models few-shot medical image understanding as a sequential agentic workflow. ARISE structures agent execution into a transparent 5-stage workflow: hypothesis generation, image-grounded evidence summarization, evidence-conditioned refinement, symbolic verification, and final diagnosis. We apply ARISE to IBD assessment across two independent patient cohorts: wireless capsule endoscopy (WCE) images for Crohn's disease and B-mode ultrasound data for ulcerative colitis. ARISE consistently improves diagnostic performance over baseline VLMs while exposing where reasoning succeeds or fails, providing a more interpretable basis for clinical decision support and realistic deployment.
