White paper: requirements for routine data recording in the operating room

Research output: Contribution to journal › Review article › Contributed › peer-review

Contributors

  • Thomas Schnelldorfer - , Tufts University (Author)
  • Andrew A. Gumbs - , CHI de Poissy-St-Germain-en-Laye (Author)
  • Jennifer Tolkoff - , Tufts Medicine (Author)
  • Sarah Choksi - , Lenox Hill Hospital (Author)
  • Jessica Stockheim - , Otto von Guericke University Magdeburg (Author)
  • Amin Madani - , University of Toronto (Author)
  • Carla M. Pugh - , Stanford University (Author)
  • Takeaki Ishizawa - , Osaka Metropolitan University (Author)
  • Stefanie Speidel - , National Center for Tumor Diseases (Partners: UKD, MFD, HZDR, DKFZ) (Author)
  • Lee L. Swanström - , Institute of Image-Guided Surgery (Author)
  • Bettina M. Rau - , Klinikum Neumarkt in der Oberpfalz (Author)
  • Amir Szold - , Assuta Medical Center (Author)
  • Fabio Ausania - , University of Barcelona (Author)
  • Filippo Filicori - , Lenox Hill Hospital, Northwell Health System (Author)
  • Roland Croner - , Otto von Guericke University Magdeburg (Author)
  • S. Vincent Grasso - , University of New Mexico (Author)

Abstract

This white paper documents the consensus opinion of the authors and Artificial Intelligence Surgery editorial board members regarding common requirements needed to implement routine recording of data in the operating room. The statements were agreed upon by all authors and they attempted to outline common barriers that need to be addressed when implementing such recordings.

Details

Original languageEnglish
Pages (from-to)7-22
Number of pages16
JournalArtificial Intelligence Surgery
Volume4
Issue number1
Publication statusPublished - Mar 2024
Peer-reviewedYes

External IDs

ORCID /0000-0002-4590-1908/work/163294133

Keywords

ASJC Scopus subject areas

Keywords

  • automated features, cloud-based storage, coding of data, data access, Data recording, deidentification, domain-specific interconnectivity