Developing robust benchmarks for driving forward AI innovation in healthcare

Machine learning technologies have seen increased application to the healthcare domain. The main drivers are openly available healthcare datasets, and a general interest from the community to use its powers for knowledge discovery and technological advancements in this more conservative field. However, with this additional volume comes a range of questions and concerns — are the obtained results meaningful and conclusions accurate; how do we know we have improved state of the art; is the clinical problem well defined and does the model address it? We reflect on key aspects in the end-to-end pipeline that we believe suffer the most in this space, and suggest some good practices to avoid reproducing these issues.