揭示GPT-4V在USMLE问题上的高准确度背后隐藏的挑战:观察性研究
Zhichao Yang1, Zonghai Yao1, Mahbuba Tasmin1
1College of Information and Computer Science, University of Massachusetts Amherst, Amherst, MA, United States.
Journal of medical Internet research
|February 7, 2025
概括
GPT-4视觉 (GPT-4V) 在医学图像解释问题上表现出高准确度,但在不正确时,在解释质量和图像理解方面存在困难. 在临床整合之前需要进一步评估.
科学领域:
- 人工智能在医学中的应用
- 医学图像分析 医学图像分析
- 大型语言模型
背景情况:
- 最近的AI进步,如GPT-4,在基于文本的医学检查中显示出有希望.
- 人工智能模型解释医疗图像的能力在很大程度上仍未被探索.
- 在多模式临床场景中评估AI对于评估准备度至关重要.
研究的目的:
- 在基于图像的医学问题上评估GPT-4视觉 (GPT-4V),GPT-4和GPT-3.5轮.
- 评估GPT-4V的性能,可解释性和理解临床图像的局限性.
- 确定潜在的缺陷,并准备好用于多式联络AI的临床整合.
主要方法:
- 这是一项横截面研究,使用227个选择题和USMLE,DRQCE和AMBOSS的图像.
- 与GPT-4和GPT-3.5 Turbo相比,GPT-4V的准确性进行比较.
- 使用人类偏好和定性指标评估解释质量;模拟路边咨询.
主要成果:
- 在基于图像的问题上,GPT-4V获得了高精度 (73.1%-88.9%),超过了GPT-4和GPT-3.5 Turbo.
- 正确的答案有专家级的解释,但错误的答案显示质量差,包括图像误解.
- 对于复杂的案例,GPT-4V需要特定提示,并且通过专家的协助显示出更低的错误率.
结论:
- GPT-4V显示了医学图像解释评估的巨大潜力.
- 对错误答案的解释质量和图像解释方面的缺陷需要注意.
- 在临床部署之前,需要进行超越多选项问题的全面评估.
关键词:
聊天GPT 聊天GPT 聊天在 GPT 中,GPT 必须是 GPT.在GPT-4V中使用.在法学士 (LLM) 课程中.医学执照考试 医学执照考试在NLP中,我们使用了NLP.美国米勒 (USMLE)美国医疗执照考试人工智能的人工智能是人工智能.大型语言模型医学图像解释解释 医学图像解释自然语言处理自然语言处理.更多相关视频
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