视觉语言的大型学习模型GPT4V准确地分类了波士顿肠道准备量表的得分
Daniel Yan Zheng Lim1,2,3, Yu Bin Tan4, Jonas Ren Yi Ho4
1Dept of Gastroenterology and Hepatology, Singapore General Hospital, Singapore limyzd@gmail.com.
BMJ open gastroenterology
|March 4, 2025
概括
视觉语言的大型学习模型 (LLM) 可以在最小的训练数据下准确地得分波士顿肠道准备量表 (BBPS). 这种人工智能方法为医学图像分类提供了一个新的范式,特别是在罕见疾病中.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 胃肠病学 胃肠病学
背景情况:
- 大型学习模型 (LLM) 是先进的人工智能,最初用于自然语言处理,现在适用于多模式任务.
- 波士顿肠道准备量表 (BBPS) 评分是评估结肠清洁的临床相关任务.
- 传统的人工智能需要大量的数据;本研究以有限的例子探索了LLM的潜力.
研究的目的:
- 评估视觉语言LLM的有效性,用于自动化波士顿肠道制备量表 (BBPS) 的分级.
- 为了确定LLMs是否可以在BBPS分类中以较少的培训示例与传统AI方法相比,实现高准确度.
主要方法:
- 使用GPT4V,一个视觉语言的LLM从OpenAI,通过API.
- 采用了一个标准化的提示符,用于BBPS评分的上下文引用.
- 在HyperKvasir数据集上测试了自动BBPS分类的性能.
主要成果:
- 在1794张图像上,GPT4V取得了98%的有效结果.
- 准确度为两类 (BBPS 0-1 vs 2-3) 的0.84和四类 (BBPS 0,1,2,3) 的0.74分类.
- 宏观平均F1分数分别为0.81和0.63,与传统方法相比较有利.
结论:
- 视觉语言LLM可以在没有大型培训数据集的情况下准确执行BBPS分类.
- 这表明AI在医学中的范式转变,特别是对于数据有限的疾病.
- 在数据稀缺的医疗场景中,LLM为AI分类提供了有希望的方法.
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