在骨科培训中比较开源视觉语言模型:与居民进行比较,域特定评估和参数缩放
Sunho Ko1, Jaewook Lee2, Kyunga Ko3
1Department of Orthopedic Surgery, Seoul National University Hospital, Seoul, Korea.
Clinics in orthopedic surgery
|February 6, 2026
概括
开源视觉语言模型 (VLMs) 在骨科护理中表现有前途,大型模型与住院人员的表现相匹配. 然而,较小的模型和特定的子专业,如脊柱和儿科,需要进一步开发.
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
- 整形外科教育 整形外科教育
背景情况:
- 推进骨科护理需要多式人工智能,包括图像处理和安全部署.
- 目前的文献对这些应用程序的开源视觉语言模型 (VLMs) 进行了不充分的探索.
- 安全的内部运营和多式联络能力对于人工智能在骨科整合至关重要.
研究的目的:
- 使用骨科培训内检查 (OITE) 将开源VLM与骨科住院医生进行比较.
- 评估骨科子专业的VLM性能.
- 调查VLM参数大小与性能之间的关系.
主要方法:
- 通过2023年OITE (210个问题,111个带图像) 评估了六个不同大小的开源VLM.
- 将模型性能与2023年OITE居民分数进行比较.
- 利用皮尔森相关性来分析模型大小与性能之间的关联.
主要成果:
- 最大的VLMs (Qwen2.5-VL-72B,Llama-3.2-90B) 的表现与第二年居民相比相当.
- 一个中型模型 (Qwen-32B) 的表现优于第一年居民;较小的模型的表现低于第一年居民.
- Qwen2.5-VL-72B在足/脚和体育医学方面表现出色;Llama-3.2-90B在基础科学和手/手腕方面领先.
- 所有模型都在脊柱和儿科问题上扎; 准确性随着大小增加到72B参数.
结论:
- 更小的VLM提供更低的硬件需求,但精度降低.
- 脊椎和儿科领域仍然是当前VLMs的挑战.
- 模型选择需要平衡临床需求与基于特定领域基准的硬件限制.
- 开源的VLM显示出潜力,但需要进一步完善用于临床和教育用途.
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