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评估在12导电心电图像解释的多式大型语言模型中零拍摄视觉问题答案的性能

Tomohisa Seki¹, Yoshimasa Kawazoe^1,2, Hiromasa Ito¹

¹Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan.

Frontiers in cardiovascular medicine

|February 21, 2025

概括

多模大型语言模型 (LLM) 显示了使用零射击视觉问答解释12导电心电图 (ECG) 的潜力. 然而,目前的模型在精确的图像特征提取方面遇到了困难,这凸显了人工智能诊断中需要改进幻觉控制的需要.

科学领域:

人工智能的人工智能
医疗信息学医疗信息学
心脏病学心脏病学

背景情况:

大型语言模型 (LLM) 正在朝着多模式发展,使各种数据类型的解释成为可能.
零拍摄视觉问题答案 (VQA) 为人工智能驱动的图像分析提供了一个有希望的途径,而不需要特定任务的培训.
12导电心电图 (ECG) 是心脏病学的关键诊断工具,有可能通过人工智能进行增强的解释.

研究的目的:

为了评估零射击VQA的诊断性能,在12导电心电图像上使用多式LLM.
评估当前多式联络LLM在分析复杂的医学成像数据方面的能力和局限性.
确定医疗诊断应用中人工智能模型的改进领域.

主要方法:

应用零射击VQA与多式LLM对12导电心电图像数据集的应用.
基于特征提取,先决条件描述和逻辑推理的准确性来评估模型性能.
对模型正确回答问题的能力与其描述性推理的准确性进行比较分析.

主要成果:

多模式LLM在提取和口述特定的ECG图像特征时表现出错误的趋势.
与详细的图像特征分析相比,模型在描述先决条件和做出逻辑推理方面表现更好.
即使是正确的答案也经常伴随着对ECG图像中的视觉元素的不准确描述.
该研究在LLMs产生的反应中发现了图像幻觉的重大问题.

结论:

目前的多模式LLM需要加强对图像幻觉的控制,以便在医学诊断中可靠地应用.
仅仅基于多选格式的正确答案的绩效评估可能在涉及医疗图像的VQA任务中不足.
需要进一步的研究来完善LLM在准确解释和描述ECG等视觉医疗数据方面的能力.

关键词:

电心电图 (ECG) 是一种心电图.幻觉幻觉是一种幻觉.大型语言模型视觉问题回答回答问题零射击学习的学习

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