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Licheng Jiao¹, Jiayao Hao¹, Ruiyang Li¹

¹School of Artificial Intelligence, Xidian University, Xi'an, China.

Research (Washington, D.C.)

|March 2, 2026

概括

基础模型 (FMs) 通过实现多模式数据集成和任务不可知转移,克服注释限制,推进医学深度学习. 本综述系统地分析了医疗FM,其应用,以及未来发展的挑战.

科学领域:

医疗成像医学成像
人工智能的人工智能
深度学习 (Deep Learning) 是一种深度学习.

背景情况:

医学深度学习面临的挑战是有限的注释数据和不良的模型概括.
基础模型 (FMs) 通过大规模的预训练和医疗图像解释的高效微调提供了一个范式的转变.
FMs能够实现多模式表示和任务不可知转移,适应各种应用而无需广泛的再培训.

研究的目的:

系统地审查医疗FM的研究进展,重点关注任务,数据集和评估指标.
分类和分析主流医疗FM,比较其性能和特征.
通过多个维度识别医疗FM的关键挑战和新兴趋势.

主要方法:

医学FM研究的综合文献综述.
整合和汇总多源数据 (2D/3D成像,电子健康记录等) 和评估指标.
预训练,视觉,视觉语言和多模式FM的分类和分析.
介绍和验证IPIU医疗FM平台.

主要成果:

FMs促进关键的解释任务,如分类,细分,生成和预测预后.
分析涵盖了各种数据类型和主流的FM架构.
拟议的IPIU平台在临床任务中证明了其有效性.
对12个关键维度 (数据,建模,安全等) 的系统分析. 为医疗FMs是呈现的.

结论:

医疗FM代表了医学图像解释的重大进步,解决了数据限制.
本综述提供了该领域的系统概述和前性分析.
这些发现为医疗FM的可持续发展提供了理论支持和实际参考.

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