在Gram染色和细菌形状识别方面,GPT-4o和Gemini 1.5 Pro的功能
Joya-Rita Hindy1, Tarek Souaid1, Christopher S Kovacs2
1Department of Internal Medicine, Cleveland Clinic, Cleveland, OH 44195, USA.
Future microbiology
|July 29, 2024
概括
大型语言模型 (LLM) 显示了从Gram染色图像分类细菌的不同准确性. 虽然GPT-4o的性能总体上优于Gemini 1.5 Pro,但两者都没有为临床微生物学实施做好准备.
科学领域:
- 微生物学 微生物学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 准确的细菌分类对于有效的临床治疗至关重要.
- 大型语言模型 (LLM) 越来越多地被用于医学图像分析.
研究的目的:
- 为了评估GPT-4o和Gemini 1.5 Pro的视觉分类准确度,使用Gram染色图像.
- 为了比较这些LLM在区分格拉姆阳性细菌和格拉姆阴性细菌以及识别它们的形状 (菌或细菌) 的性能.
主要方法:
- 80个标记的格拉姆染色图像被用来测试GPT-4o和Gemini 1.5 Pro.
- 性能评估基于正确识别格拉姆染色特性和细菌形态.
主要成果:
- 对于特定的细菌 (如黄金杆菌),GPT-4o的准确度达到100%,而Gemini 1.5 Pro的结果则有所变化 (例如,Clostridium perfringens的45%).
- 这两种模型都扎着Neisseria gonorrhoeae,无法识别Gram染色和形状.
- 与Gemini 1.5 Pro.相比,GPT-4o在大多数分类中表现优越或相等.
结论:
- 目前的未预备的LLM对于临床微生物学应用来说不够准确.
- 需要对更大的数据集进行进一步的研究,以提高微生物分类中的LLM性能.
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