卫生系统的学习实现了通用神经成像模型
Akhil Kondepudi1,2, Akshay Rao1, Chenhui Zhao1,3
1Machine Learning in Neurosurgery Lab, University of Michigan.
Research square
|December 11, 2025
概括
在临床神经成像数据上训练的NeuroVFM等人工智能模型的健康系统学习显著提高了医疗任务的性能. 这种方法提高了诊断准确性和报告生成,提供了更安全的临床决策支持.
科学领域:
- 医疗人工智能 医疗人工智能
- 神经成像分析分析 神经成像分析
- 基金会模型 基金会模型
背景情况:
- 在公共数据上训练的前沿AI模型缺乏临床数据的访问权限.
- 由于隐私问题,神经成像数据在公共数据集中不足.
- 这限制了当前AI在临床医学中的表现.
研究的目的:
- 评估前沿人工智能模型在神经成像任务中的性能.
- 引入和验证一个新的范式:卫生系统学习.
- 开发一个高性能,通用的神经成像AI模型.
主要方法:
- 开发了NeuroVFM,这是一个在524万份临床MRI和CT卷上训练的视觉基础模型.
- 使用可扩展的体积联合嵌入预测架构.
- 采用轻量级的视觉指令调整,将NeuroVFM与语言模型配对.
主要成果:
- 在包括诊断和报告生成在内的神经成像任务中,NeuroVFM实现了最先进的性能.
- 该模型展示了新兴的神经解剖学理解和可解释的视觉接地.
- 通过NeuroVFM生成的报告在准确性,分类和专家偏好方面超过了前沿模型,并减少了错误.
结论:
- 卫生系统学习是创造通用医学AI的可行范式.
- 神经VFM为临床基础模型提供了一个可扩展的框架.
- 这种方法提高了AI的安全性和医疗保健中的临床决策支持.
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