Author summary The field of machine learning has made significant technical advancements over the past several years, but the impact of this technology on healthcare practice has remained limited. We identify issues in the structure of the field of machine learning for healthcare which incentivise work that is scientifically novel over work that ultimately impacts patients. Among others, these issues include a lack of diversity in available data, an emphasis on targets which are easy to measure but may not be clinically important, and limited funding for work focused on deployment. We offer a series of suggestions about how best to address these issues, and advocate for a distinction to be made between “machine research performed ON healthcare data” and true “machine FOR healthcare”. The latter, we argue, requires starting from the very beginning with a focus on the impact that a model will have on patients. We conclude with discussion of “impact challenges”—specific and measurable goa