聊天GPT-4o和双子座在诊断放射学培训内考试中的高级表现的比较分析
Kian A Huang1, Haris K Choudhary1, William M Hardin1
1Radiology, USF Health Morsani College of Medicine, Tampa, USA.
Cureus
|April 21, 2025
概括
大型语言模型 (LLMs) 显示了放射学培训的潜力,ChatGPT-4o在诊断放射学考试中表现优于Gemini Advanced. 然而,这两种模型都需要改善用于临床使用的图像解释.
科学领域:
- 人工智能在医学中的应用
- 医疗教育 技术 技术 医学教育
- 放射学 诊断 诊断 放射学
背景情况:
- 人工智能越来越多地融入医学教育和临床实践.
- 对用于诊断推理和培训的大型语言模型 (LLM) 的兴趣日益增加.
- 在分析书面和基于图像的放射学内容时,关于LLM准确性的不确定性.
研究的目的:
- 评估ChatGPT-4o和Gemini Advanced在2022年ACR诊断放射学内部培训 (DXIT) 考试中的表现.
- 评估不同放射学子领域的LLM能力.
- 将AI模型在解释书面和基于图像的放射学问题的性能进行比较.
主要方法:
- 在2022年DXIT考试中的106个选择题中测试ChatGPT-4o和Gemini Advanced.
- 包括各种放射学子专业的图像和书面问题.
- 使用整体准确性,子领域特定准确性和双比例z测试来比较性能.
主要成果:
- 聊天GPT-4o实现了69.8%的整体准确率,超过双子座高级 (60.4%),尽管在统计学上并不显著.
- 与Gemini Advanced (43.8%) 相比,ChatGPT-4o在基于图像的问题上显示出更高的准确性 (57.8%).
- 两种模型在基于书面的问题上都表现出类似的准确性 (88.1%和85.7%),并且在各个子领域的表现各不相同.
结论:
- 士课程对放射学教育和基于文本的评估充满希望.
- 图像解释精度和响应一致性的局限性需要进一步改进人工智能模型.
- 未来的研究应该集中在提高人工智能的可靠性,多式联运能力和整合到放射学培训中.
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