基于文本和图像的ACR诊断放射学的视觉GPT-4的表现 培训中的检查问题
Nolan Hayden1, Spencer Gilbert1, Laila M Poisson1
1From the Department of Diagnostic Radiology, Henry Ford Health, 2799 W Grand Blvd, Detroit, MI, 48202 (N.H., B.G., C.K.); Michigan State University College of Osteopathic Medicine, East Lansing, Mich (S.G.); and Department of Public Health Sciences, Henry Ford Health, Michigan State University Health Sciences, Detroit, Mich (L.M.P.).
在基于文本的放射学问题中,GPT-4V显示出有希望的结果,但在图像解释方面却存在困难,这凸显出需要进一步发展人工智能在医学中的视觉诊断能力.
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:
- 像ChatGPT (生成预训练变压器) 这样的大型语言模型 (LLM) 的进步为放射学提供了新的潜力.
- 在放射学特定任务中,LLM的基线性能仍未得到充分研究.
- 有视觉的GPT-4 (GPT-4V) 提供了一个评估医疗诊断中的AI能力的机会.
研究的目的:
- 评估GPT-4V在放射学培训中的考试问题上的表现.
- 评估模型在放射学方面的基线知识,包括基于图像和只有文本的问题.
- 测量人工智能在支持放射学教育和实践方面的潜力.
主要方法:
- GPT-4V (2023年9月发布) 在386名退休的美国放射学学院诊断放射学培训内检查问题上进行了测试.
- 评估包括189个基于图像的和197个仅基于文本的问题.
- 部分分析探讨了不同零射击提示对性能的影响,使用千平方和麦克纳马测试进行统计分析.
主要成果:
- GPT-4V正确回答了65.3%的独特问题 (246/377).
- 纯文本问题 (81.5%) 的准确性明显高于以图像为基础的问题 (47.8%).
- 连锁思维促使基于文本的问题的表现得到改善,但对于基于图像的问题没有注意到即时差异.
结论:
- GPT-4V在基于文本的放射学知识方面表现出能力,但在解释放射图像方面存在重大缺陷.
- 这些发现表明,目前的AI模型需要进一步改进,以便在医学图像分析中得到强大的应用.
- 需要进一步的研究来增强AI在放射学中的视觉诊断能力.
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