MODERN healthcare has become smarter, benefitting from the use of technology like artificial intelligence (AI), in which machine learning (ML) models “learn” how to make decisions based on patterns found in large sets of patient data. This has in turn helped improve the accuracy of medical diagnoses, as well as accelerate the research and development of critical medicines.

However, experts in recent years realised that the traditional process of developing ML applications through the centralised collection of data is insufficient, as effective ML models for healthcare require more data than what would be freely shared due to issues in security and privacy. These challenges have prevented AI from taking the healthcare industry to the next level, where models that achieve clinical-grade accuracy can only be derived from sufficiently large, diverse, and curated datasets.

To democratise AI and reap the benefits of data in healthcare, there is a need for a training method for ML models that is not subject to the risks of sharing sensitive data outside the institution that holds it. Federated learning provides such a method.