Institute of Ophthalmology, University College London, London, UK.
NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK.
Br J Ophthalmol. 2024 Sep 20;108(10):1341-1348. doi: 10.1136/bjo-2024-325459.
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.
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义模型发展为通用、可泛化的模型，能够适应各种不同的应用。眼科作为一个专业领域，有可能成为其他医学专业的典范，为广泛将基础模型整合到临床实践中提供蓝图。本文希望为眼科专业人员提供一份指南，帮助他们更好地理解基础模型，并为读者提供工具，以探索在自己的研究和实践中使用基础模型。我们首先概述了实现这些模型开发的关键概念和技术进步，介绍了新颖的训练方法和现代 AI 架构。接下来，我们总结了眼科领域基础模型的现有文献，涵盖了视觉基础模型、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、偏差和临床验证相关的主要挑战，并提出了关键步骤，以最大限度地发挥这项强大技术的优势。
