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基于医学图像的评估和对医学教育的影响

Xiang Yang¹, Wei Chen^2,3

¹Department of Neurosurgery, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang Alley, Wu Hou Distract, Chengdu, Sichuan Province, 610037, China.

BMC medical education

|August 23, 2025

概括

像GPT-4o这样的生成人工智能 (AI) 模型在基于图像的医疗许可证考试中表现出色. 这些人工智能工具可以帮助医学教育, 但专家的监督对于准确性至关重要.

科学领域:

医疗教育中的人工智能
医疗保健中的大型语言模型 (LLM)
医疗评估技术

背景情况:

像ChatGPT这样的生成人工智能工具对医学教育充满希望.
大型语言和视觉模型在基于图像的医学测试中的性能尚未得到充分研究.
这限制了AI在医学上的更广泛的教育实用性.

研究的目的:

评估GPT-4和GPT-4Omni (GPT-4o) 在基于图像的美国医学执照考试 (USMLE) 样本项目上的表现.
探索这些人工智能模型对医学教育的影响.
用医疗图像评估人工智能在基于案例的教学中的有用性.

主要方法:

从USMLE第一步和第二步的临床知识样本组中识别出基于图像的问题.
应用快速工程技术来产生GPT-4和GPT-4o的响应.
在基于案例的教学场景中计算准确率和探索AI应用.

主要成果:

在38个基于图像的USMLE问题上,GPT-4o的准确度达到了89.5%,超过了GPT-4的73.4%.
模型在问题复杂性方面存在分歧;GPT-4o协助分析和修改错误的答案.
GPT-4o显示了教学规划的潜力,尽管人类监督是必不可少的.

结论:

GPT模型可以准确地回答基于图像的医学检查问题,GPT-4o显示出更高的性能.
快速工程提高了人工智能在医疗教学规划中的实用性.
人工智能对医学教育具有前景, 但专家监督对于可靠性至关重要.

关键词:

聊天GPT 大型语言模型医疗教育医疗检查

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