综述医学影像基础模型的发展现状与挑战。
Foundation Models for Medical Imaging: Status, Challenges, and Directions
- 梳理基础模型的核心设计原理与跨模态应用方法
- 总结当前在多任务、多器官上的临床应用进展
- 指出可信性与临床落地的未来关键挑战
基础模型（FMs）正迅速重塑医学影像领域，推动从专用于特定任务的小型网络向可跨模态、跨解剖结构和临床任务灵活适配的大规模通用模型转变。本文从三个核心维度综述医学影像基础模型的最新发展：基础模型的设计原则、在医学影像中的应用实践，以及面向未来的挑战与机遇。整体而言，该综述为开发既强大又可靠、具备临床转化潜力的基础模型提供了技术扎实、临床敏感且面向未来的路线图。
原文摘要 · Abstract (English)
Foundation models (FMs) are rapidly reshaping medical imaging, shifting the field from narrowly trained, task-specific networks toward large, general-purpose models that can be adapted across modalities, anatomies, and clinical tasks. In this review, we synthesize the emerging landscape of medical imaging FMs along three major axes: principles of FM design, applications of FMs, and forward-looking challenges and opportunities. Taken together, this review provides a technically grounded, clinically aware, and future-facing roadmap for developing FMs that are not only powerful and versatile but also trustworthy and ready for responsible translation into clinical practice.
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