使用多式大型语言模型评估性结肠炎的梅奥内镜评分:人类盲目的准确性研究
Antonietta Gerarda Gravina1, Raffaele Pellegrino1, Giuseppe Imperio1
1Division of Hepatogastroenterology, Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy.
Intestinal research
|February 12, 2026
概括
像ChatGPT-4o这样的大型语言模型显示了从内镜图像中分配梅奥内镜分数 (MES) 的轻微潜力. 需要进一步开发以提高临床应用中的准确性.
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
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 的进步使图像处理能力成为可能.
- 研究LLM在内镜临床评分中的实用性.
研究的目的:
- 评估ChatGPT-4o准确分配梅奥内镜评分 (MES) 的能力.
- 评估基于LLM的图像分析与专家内镜师评估的性能.
主要方法:
- 选择一组内镜框架被用来比较四个输入模型.
- 在304框架的扩展套件上确认了性能最高的模型.
- 使用科恩的卡帕和斯皮尔曼的罗,评估了对应性.
主要成果:
- 一个模型与专家得分有显著的一致性 (κ = 0.260).
- 在扩展集中,绝对一致性达到45.3%.
- 对中度至重度疾病的敏感度为73%,90%的预测值为负值.
结论:
- 在内镜镜中,ChatGPT-4o对MES评估具有温和的潜力.
- 进一步的模型改进对于临床实用性和提高准确性至关重要.
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