In the past decade, the application of machine learning (ML) to healthcare has helped drive the automation of physician tasks as well as enhancements in clinical capabilities and access to care. This progress has emphasized that, from model development to model deployment, data play central roles. In this Review, we provide a data-centric view of the innovations and challenges that are defining ML for healthcare. We discuss deep generative models and federated learning as strategies to augment datasets for improved model performance, as well as the use of the more recent transformer models for handling larger datasets and enhancing the modelling of clinical text. We also discuss data-focused problems in the deployment of ML, emphasizing the need to efficiently deliver data to ML models for timely clinical predictions and to account for natural data shifts that can deteriorate model performance.

在过去的十年中，机器学习 (ML) 在医疗保健中的应用帮助推动了医生任务的自动化以及临床能力和护理机会的增强。这一进展强调了从模型开发到模型部署，数据发挥着核心作用。在这篇评论中，我们提供了以数据为中心的视角，来了解定义医疗保健领域的机器学习的创新和挑战。我们讨论深度生成模型和联合学习作为增强数据集以提高模型性能的策略，以及使用更新的变压器模型来处理更大的数据集和增强临床文本的建模。我们还讨论了 ML 部署中以数据为中心的问题，