在基于图像的核心放射病例中,ChatGPT-4v和ChatGPT-4o的性能有限
Romi Noy Achiron1, Shmuel Kagasov2, Rina Neeman1
1Department of Radiology, Tel Aviv Sourasky Medical Center, 6 Weizmann St., Tel Aviv, Israel; Faculty of Medicine, Tel Aviv University, P.O.B 39040, Tel Aviv, Israel.
像ChatGPT-4o和ChatGPT-4v这样的新人工智能模型对放射学委员会考试的解释有希望,但尚未达到通过标准. 为了安全的临床使用,需要进一步的研究.
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
- 放射学教育 放射学教育
背景情况:
- 大型语言模型 (LLM) 展示了临床推理和解释医学图像的能力.
- 最近的进展包括具有视觉处理的LLM,使得文本和图像分析结合起来.
研究的目的:
- 评估ChatGPT-4v和ChatGPT-4o在解释国家放射学委员会考试中的基于图像的多选择题方面的表现.
- 在核心放射学场景中识别这些AI模型的局限性.
主要方法:
- 一项前性研究利用了2020-2024年国家放射学委员会考试中的222个基于图像的多选题.
- 聊天GPT-4v和聊天GPT-4o处理了问题,并将其生成的答案与官方答案密钥进行了比较.
- 根据放射学子专业和包括临床信息来分析精度.
主要成果:
- 聊天GPT-4o取得了59%的成功率,聊天GPT-4v取得了54%的成功率,这两者都低于董事会考试通过标准.
- 在ChatGPT-4v和ChatGPT-4o之间没有发现显著的性能差异 (P=0.339).
- 这两种模型在含有临床信息的问题上表现更好 (ChatGPT-4v:63.8%,ChatGPT-4o:67.0%) 与没有临床信息的问题相比 (ChatGPT-4v:46.1%,ChatGPT-4o:52.3%).
结论:
- 目前的ChatGPT版本显示出作为补充诊断辅助工具的潜力,但不符合板级图像解释所需的准确性.
- 跨子专业的绩效变化表明了局限性和进一步发展的需要.
- 在这些AI工具可以安全地集成到临床实践中之前,需要进行广泛的研究.
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