# Surgical Data Science: Enabling Next-Generation Surgery

**Authors:** Lena Maier-Hein, Swaroop Vedula, Stefanie Speidel, Nassir Navab, Ron, Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain, Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes, Kenngott, Michael Kranzfelder, Anand Malpani, Keno M\"arz, Thomas Neumuth,, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor,, Martin Wagner, Gregory D. Hager, Pierre Jannin

arXiv: 1701.06482 · 2018-05-17

## TL;DR

This paper defines Surgical Data Science as a new discipline that leverages large-scale data and machine learning to improve decision-making and quality in interventional medicine, based on expert consensus.

## Contribution

It establishes a formal definition for Surgical Data Science, discusses key challenges and opportunities, and outlines a roadmap for future research in the field.

## Key findings

- Consensus definition of Surgical Data Science
- Identification of key challenges and opportunities
- Proposed roadmap for advancing the field

## Abstract

This paper introduces Surgical Data Science as an emerging scientific discipline. Key perspectives are based on discussions during an intensive two-day international interactive workshop that brought together leading researchers working in the related field of computer and robot assisted interventions. Our consensus opinion is that increasing access to large amounts of complex data, at scale, throughout the patient care process, complemented by advances in data science and machine learning techniques, has set the stage for a new generation of analytics that will support decision-making and quality improvement in interventional medicine. In this article, we provide a consensus definition for Surgical Data Science, identify associated challenges and opportunities and provide a roadmap for advancing the field.

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Source: https://tomesphere.com/paper/1701.06482