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MedVH:在医学背景下对大视觉语言模型的系统评估

Zishan Gu^1,2, Jiayuan Chen^1,2, Fenglin Liu³

¹Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, USA.

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)

|August 22, 2025

概括

医疗大视觉语言模型 (LVLMs) 显示出高幻觉倾向,甚至比一般模型更高. 需要像MedVH这样的新评估框架来确保临床环境的可靠性.

科学领域:

人工智能
医疗信息学
计算机视觉

背景情况:

大视觉语言模型 (LVLM) 在自然图像和文本任务中表现出色.
在临床环境中对LVLM的强度不足.
现有的评估没有充分解决医疗特异性幻觉.

研究的目的:

介绍医学视觉幻觉测试 (MedVH) 的框架.
评估医疗和一般 LVLM 的幻觉倾向.
为未来可靠的医疗人工智能研究提供基础.

主要方法:

开发了一种新的医疗视觉幻觉评估框架.
包含六个任务:两个传统任务和四个新任务 (多选项VQA,长时间响应生成).
在各种LVLM中实验了六个评估指标.

主要成果:

医疗 LVLM 具有显著的幻觉易感性,通常超过一般模型.
尽管在标准的医疗任务中表现强,但特定领域的模型显示出更高的幻觉率.
这突显了有关专业医疗人工智能的可靠性.

结论:

医疗 LVLM 需要强有力的推理来防止幻觉并准确地整合医学知识.
在没有微调的情况下缓解幻觉.
在PhysioNet上提供MedVH数据集以进行进一步研究.

关键词:

大视觉语言模型医疗幻觉评估多模式推理

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