The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization and translatable clinical decision support systems. Among the key issues raised in the report, data availability, the need for novel computing architectures and explainable artificial intelligence algorithms are still relevant, despite the tremendous progress made over the past few years alone. Furthermore, translational goals of data sharing, validation of performance for regulatory approval, generalizability and mitigation of unintended bias must be accounted for early in the development process. In this Perspective, we explore challenges unique to high-dimensional clinical imaging data, in addition to highlighting some of the technical and ethical considerations involved in developing machine learning systems that better represent the high-dimensional nature of many imaging modalities. Furthermore, we argue that methods that attempt to address explainability, uncertainty and bias should be treated as core components of any clinical machine learning system.

美国国立卫生研究院于 2018 年确定了医学成像人工智能未来的关键重点领域，为图像采集、算法、数据标准化和可翻译临床决策支持系统的研究制定了基础路线图。在报告中提出的关键问题中，数据可用性、对新颖计算架构的需求和可解释的人工智能算法仍然相关，尽管仅在过去几年就取得了巨大进展。此外，必须在开发过程的早期考虑数据共享的转化目标、监管批准的性能验证、普遍性和减轻意外偏差。在这个视角中，我们探索了高维临床影像数据所特有的挑战，除了强调开发机器学习系统所涉及的一些技术和伦理考虑之外，这些系统更好地代表了许多成像模式的高维性质。此外，我们认为试图解决可解释性、不确定性和偏见的方法应该被视为任何临床机器学习系统的核心组成部分。