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.

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