Synthetic data in machine learning for medicine and healthcare

As artificial intelligence (AI) for applications in medicine and healthcare undergoes increased regulatory analysis and clinical adoption, the data used to train the algorithms are undergoing increasing scrutiny. Scrutiny of the training data is central to understanding algorithmic biases and pitfalls. These can arise from datasets with sample-selection biases -- for example, from a hospital that admits patients with certain socioeconomic backgrounds, or medical images acquired with one particular type of equipment or camera model. Algorithms trained with biases in sample selection typically fail when deployed in settings sufficiently different from those in which the trained data were acquired1. Biases can also arise owing to class imbalances -- as is typical of data associated with rare diseases -- which degrade the performance of trained AI models for diagnosis and prognosis.