Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

数据驱动的机器学习 (ML) 已成为一种很有前途的方法，可根据现代医疗保健系统大量收集的医疗数据构建准确且强大的统计模型。现有的医疗数据没有被机器学习充分利用，主要是因为它位于数据孤岛中，而隐私问题限制了对这些数据的访问。然而，如果无法获得足够的数据，机器学习将无法充分发挥其潜力，并最终无法从研究过渡到临床实践。本文考虑了导致这一问题的关键因素，探讨了联邦学习 (FL) 如何为数字健康的未来提供解决方案，并强调了需要解决的挑战和注意事项。