  Recent developments in data science in general and machine learning in
particular have transformed the way experts envision the future of surgery.
Surgical Data Science (SDS) is a new research field that aims to improve the
quality of interventional healthcare through the capture, organization,
analysis and modeling of data. While an increasing number of data-driven
approaches and clinical applications have been studied in the fields of
radiological and clinical data science, translational success stories are still
lacking in surgery. In this publication, we shed light on the underlying
reasons and provide a roadmap for future advances in the field. Based on an
international workshop involving leading researchers in the field of SDS, we
review current practice, key achievements and initiatives as well as available
standards and tools for a number of topics relevant to the field, namely (1)
infrastructure for data acquisition, storage and access in the presence of
regulatory constraints, (2) data annotation and sharing and (3) data analytics.
We further complement this technical perspective with (4) a review of currently
available SDS products and the translational progress from academia and (5) a
roadmap for faster clinical translation and exploitation of the full potential
of SDS, based on an international multi-round Delphi process.
