一个完全开放的AI基础模型应用于胸部X射线
DongAo Ma1, Jiaxuan Pang1, Michael B Gotway2
1School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
Nature
|June 11, 2025
概括
通过利用来自多个数据集的多样性专家知识,Ark+是一种新的胸部放射学基础模型,提高了诊断准确性和适应性. 这种开源人工智能模型改善了医疗成像中的现有深度学习局限性.
科学领域:
- 医疗成像中的人工智能
- 放射学的深度学习
- 在医疗保健中的基础模式
背景情况:
- 胸部放射是诊断肺部疾病的主要工具.
- 目前用于胸部X射线的深度学习模型在范围,通用性和强度方面存在局限性.
- 需要先进的人工智能解决方案来提高放射学诊断的准确性和效率.
研究的目的:
- 开发Ark+,一个胸部X光学基础模型,以克服现有的深度学习模型的局限性.
- 提高诊断范围,概括性,适应性和胸部疾病解释的稳定性.
- 创建一个开源,可扩展的医疗成像AI模型.
主要方法:
- 使用胸部X射线图的基础模型方法开发Ark+.
- 通过在多个数据集中循环积累和重复使用来自异质专家标签的知识来预训练模型.
- 纳入策略来处理数据偏差,长尾分布,并支持隐私的联合学习.
主要成果:
- 在诊断胸部疾病方面,Ark+表现出卓越的性能,扩大了诊断范围,减少了误诊.
- 该模型显示适应不断变化的诊断需求,可以从少数样本中学习罕见的情况,并且在不需要再培训的情况下转移知识.
- 通过汇集多样化的数据集和专家知识,实现了前所未有的表现,超越了专有模型.
结论:
- Ark+ 代表了胸部放射学人工智能的重大进步, 提供了更好的诊断能力和适应性.
- 在多样化,异构数据上训练的开放基础模型可以超过专有模型,促进医学AI的开放科学.
- 发布Ark+代码和模型有助于微调,本地适应和进一步改进,使医疗保健中的人工智能民主化.
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