Impact of data on generalization of AI for surgical intelligence applications

Omri Bar, Daniel Neimark, Maya Zohar, Gregory D. Hager, Ross Girshick, Gerald M. Fried, Tamir Wolf, Dotan Asselmann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence.

Original languageEnglish (US)
Article number22208
JournalScientific reports
Issue number1
StatePublished - Dec 2020

ASJC Scopus subject areas

  • General


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