Bridging the implementation gap of machine learning in healthcare

Applications of machine learning on clinical data are now attaining levels of performance that match or exceed human clinicians.1–3 Fields involving image interpretation—radiology, pathology and dermatology—have led the charge due to the power of convolutional neural networks, the existence of standard data formats and large data repositories. We have also seen powerful diagnostic and predictive algorithms built using a range of other data,...