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视觉大语言模型在成像诊断中的机遇和挑战:从临床MRI中大脑转移检测的教训

Christian Nelles¹, Nour Abou Zeid¹, Robert Terzis¹

¹Institute for Diagnostic and Interventional Radiology, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany.

Diagnostics (Basel, Switzerland)

|March 14, 2026

概括

像GPT-4o和Claude Sonnet 3.5这样的视觉大语言模型 (vLLMs) 在MRI上检测大脑转移具有很高的灵敏度,但具有较低的特异性和幻觉,限制了当前的临床使用.

科学领域:

人工智能在医学中的应用
医学成像分析医学成像分析
放射学放射学是一门学科.

背景情况:

评估视觉大语言模型 (vLLMs) 的诊断准确性,以检测大脑转移.
评估GPT-4o和克劳德·索内特3.5使用组合成像和文字MRI数据.
调查癌症患者常规MRI扫描中的vLLM性能.

研究的目的:

确定GPT-4o和Claude Sonnet 3.5在识别大脑转移中的诊断准确度.
为了比较这些vLLM在灵敏度,特异性和其他诊断指标方面的性能.
评估vLLM在产生大脑转移的放射性报告中的可靠性.

主要方法:

对77名患者的100次MRI检查 (50例有,50例没有脑转移) 的回顾性分析.
在GPT-4o和Claude Sonnet 3.5.5中输入每个序列的单个代表切片与临床史.
评估vLLM生成的报告以检测准确性,过度诊断,序列识别,定位,横向性和尺寸估计.

主要成果:

两种vLLM都实现了100%的灵敏度,但特异性非常低 (GPT-4o: 8%,索内特3.5: 4%).
诊断准确性很低 (GPT-4o: 54%,索内特3.5: 52%),两种模型在12%的病例中都有幻觉病变.
序列识别很高,但解剖学定位和病变横向性显示出局限性.

结论:

GPT-4o和Claude Sonnet 3.5对大脑转移的检测具有很高的灵敏度,但由于特异性低和幻觉,它们目前不适合临床使用.
在vLLM的空间可靠性和诊断行为需要进一步的研究,特别是关于视觉和文本输入的平衡.
未来的研究应该专注于提高vLLM的特异性和可靠性,以提供准确的放射性报告.

关键词:

磁共振成像是一种磁共振成像技术.大脑转移是大脑转移.诊断的准确性诊断的准确性视觉大型语言模型

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