UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Surgical Data Science: Enabling Next-Generation Surgery

Maier-Hein, L; Vedula, S; Speidel, S; Navab, N; Kikinis, R; Park, A; Eisenmann, M; ... Jannin, P; + view all (2017) Surgical Data Science: Enabling Next-Generation Surgery. Nature Biomedical Engineering , 1 pp. 691-696. 10.1038/s41551-017-0132-7. (In press). Green open access

[img]
Preview
Text
Stoyanov_1701.06482v1.pdf - Accepted version

Download (644kB) | Preview

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.

Type: Article
Title: Surgical Data Science: Enabling Next-Generation Surgery
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41551-017-0132-7
Publisher version: http://dx.doi.org/10.1038/s41551-017-0132-7
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Health care, Surgery
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1539836
Downloads since deposit
33Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item