Empowering surgeons with integrated synthetic data: solutions for mastering complex clinical scenarios

Synthetic data generation across domains can bridge gaps between visual training, skill development, and personalized surgical planning, ultimately transforming how surgeons and artificial intelligence (AI) systems prepare for the complexities of the operating room. In this Perspective, we explore applications of synthetic data to advance surgical education and AI across three key areas: visual data synthesis for training surgeons and AI systems, surgical simulation for skill development and robotics, and digital twins for patient-specific surgical planning and guidance. These domains have largely remained siloed, but their integration has the potential to transform surgical training and AI development across the entire surgical workflow. To fully realize this potential, synthetic data must extend beyond routine surgical events to model atypical anatomy and intraoperative complications-the high-stakes clinical scenarios where enhanced training and AI support are most critical.

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Publication Details

Journal
npj Digital Medicine
Published
2026-04-30
DOI
https://doi.org/10.1038/s41746-026-02660-z
Primary Topic
Surgical Simulation and Training
Type
article
Field-Weighted Citation Impact
0.00
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article

Empowering surgeons with integrated synthetic data: solutions for mastering complex clinical scenarios

Lisa Marie Knowlton, Danush Kumar Venkatesh, Yann Sakref, Hao Ding et al.
npj Digital Medicine
Surgical Simulation and Training
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Empowering surgeons with integrated synthetic data: solutions for mastering complex clinical scenarios

Lisa Marie Knowlton, Danush Kumar Venkatesh, Yann Sakref, Hao Ding, Ruhika Iyer, Lalithkumar Seenivasan, Jeffrey K Jopling, Mathias Unberath, Stefanie Speidel
article en

Abstract

Synthetic data generation across domains can bridge gaps between visual training, skill development, and personalized surgical planning, ultimately transforming how surgeons and artificial intelligence (AI) systems prepare for the complexities of the operating room. In this Perspective, we explore applications of synthetic data to advance surgical education and AI across three key areas: visual data synthesis for training surgeons and AI systems, surgical simulation for skill development and robotics, and digital twins for patient-specific surgical planning and guidance. These domains have largely remained siloed, but their integration has the potential to transform surgical training and AI development across the entire surgical workflow. To fully realize this potential, synthetic data must extend beyond routine surgical events to model atypical anatomy and intraoperative complications-the high-stakes clinical scenarios where enhanced training and AI support are most critical.

npj Digital Medicine
Johns Hopkins University (US), Palo Alto University (US), Stanford Medicine (US), University Hospital Carl Gustav Carus (DE), Stanford University (US)
Openalex Percentile: Top 13%
Surgical Simulation and Training
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