# Surgical Data Science: A Consensus Perspective

arXiv: https://arxiv.org/abs/1806.03184 · HTML: https://mlsanity.com/paper/1806.03184/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [OperA: Attention-Regularized Transformers for Surgical Phase Recognition](https://mlsanity.com/paper/2103.03873/) (2021)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [Know your sensORs -- A Modality Study For Surgical Action Classification](https://mlsanity.com/paper/2203.08674/) (2022)
- [Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge](https://mlsanity.com/paper/2212.04448/) (2022)
- [Uncertainty-aware Self-supervised Learning for Cross-domain Technical Skill Assessment in Robot-assisted Surgery](https://mlsanity.com/paper/2304.14589/) (2023)

## Abstract (arXiv)

Surgical data science is a scientific discipline with the objective of improving the quality of interventional healthcare and its value through capturing, organization, analysis, and modeling of data. The goal of the 1st workshop on Surgical Data Science was to bring together researchers working on diverse topics in surgical data science in order to discuss existing challenges, potential standards and new research directions in the field. Inspired by current open space and think tank formats, it was organized in June 2016 in Heidelberg. While the first day of the workshop, which was dominated by interactive sessions, was open to the public, the second day was reserved for a board meeting on which the information gathered on the public day was processed by (1) discussing remaining open issues, (2) deriving a joint definition for surgical data science and (3) proposing potential strategies for advancing the field. This document summarizes the key findings.