Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice. We begin by outlining the key concepts and technological advances which have enabled the development of these models, providing an overview of novel training approaches and modern AI architectures. Next, we summarise existing literature on the topic of foundation models in ophthalmology, encompassing progress in vision foundation models, large language models and large multimodal models. Finally, we outline major challenges relating to privacy, bias and clinical validation, and propose key steps forward to maximise the benefit of this powerful technology. Data sharing not applicable as no datasets generated and/or analysed for this study.

中文翻译：

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眼科基础模型


基础模型代表了人工智能 （AI） 的范式转变，从专为特定任务设计的狭隘模型演变为适用于无数不同应用的通用、可推广模型。眼科作为一门专业有可能成为其他医学专业的典范，为将基础模型广泛整合到临床实践中提供蓝图。这篇综述希望为寻求更好地了解基础模型的眼保健专业人士提供路线图，同时为读者提供在自己的研究和实践中探索基础模型的使用的工具。我们首先概述了促成这些模型开发的关键概念和技术进步，概述了新颖的训练方法和现代 AI 架构。接下来，我们总结了关于眼科基础模型主题的现有文献，包括视觉基础模型、大型语言模型和大型多模态模型的进展。最后，我们概述了与隐私、偏见和临床验证相关的主要挑战，并提出了进一步发展的关键步骤，以最大限度地发挥这项强大技术的优势。数据共享不适用，因为没有为本研究生成和/或分析数据集。