# AI for Quality Assurance in the Operating Room

arXiv: https://arxiv.org/abs/2606.30657 · HTML: https://mlsanity.com/paper/2606.30657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 89 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos](https://mlsanity.com/paper/2109.03223/) (2021)
- [Surgical Data Science: Enabling Next-Generation Surgery](https://mlsanity.com/paper/1701.06482/) (2017)
- [Surgical Data Science -- from Concepts toward Clinical Translation](https://mlsanity.com/paper/2011.02284/) (2020)
- [Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks](https://mlsanity.com/paper/1802.08774/) (2018)
- [RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations](https://mlsanity.com/paper/1802.03243/) (2018)
- [Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks](https://mlsanity.com/paper/1805.08569/) (2018)
- [Surgical Action Triplet Detection by Mixed Supervised Learning of Instrument-Tissue Interactions](https://mlsanity.com/paper/2307.09548/) (2023)
- [The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment: Official Splits and Benchmark](https://mlsanity.com/paper/2312.12429/) (2023)
- [Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos](https://mlsanity.com/paper/1806.05573/) (2018)
- [Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases](https://mlsanity.com/paper/2203.07345/) (2022)
- [Future-State Predicting LSTM for Early Surgery Type Recognition](https://mlsanity.com/paper/1811.11727/) (2018)
- [Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos](https://mlsanity.com/paper/2301.07053/) (2023)
- [Learning from Synchronization: Self-Supervised Uncalibrated Multi-View Person Association in Challenging Scenes](https://mlsanity.com/paper/2503.13739/) (2025)
- [ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition in the Operating Room](https://mlsanity.com/paper/2312.12250/) (2023)
- [Feature Mixing Approach for Detecting Intraoperative Adverse Events in Laparoscopic Roux-en-Y Gastric Bypass Surgery](https://mlsanity.com/paper/2504.16749/) (2025)
- [The SAGES Critical View of Safety Challenge: A Global Benchmark for AI-Assisted Surgical Quality Assessment](https://mlsanity.com/paper/2509.17100/) (2025)
- [When do they StOP?: A First Step Towards Automatically Identifying Team Communication in the Operating Room](https://mlsanity.com/paper/2502.08299/) (2025)
- [CliPPER: Contextual Video-Language Pretraining on Long-form Intraoperative Surgical Procedures for Event Recognition](https://mlsanity.com/paper/2603.24539/) (2026)
- [Endoshare: A Publicly Available, Surgeons-Friendly Solution to De-Identify and Manage Surgical Videos](https://mlsanity.com/paper/2510.20087/) (2025)

## Abstract (arXiv)

Surgical outcomes depend not only on patient factors and postoperative care but are also strongly influenced by the quality of the operation itself. Yet, for much of mod-ern surgery, intraoperative quality has been assessed indirectly through outcomes and operative reports. The increase in minimally invasive procedures inherently guided by endoscopic video, together with advances in artificial intelligence, creates an unprecedented opportunity to systematically observe, measure, and improve surgi-cal care. This chapter introduces AI-enabled Surgical Quality Assurance as a frame-work for using surgical data to support continuous assessment and improvement in the operating room. We first review existing approaches to surgical safety, from sys-tem-level interventions to procedure-specific standards. We then describe how AI can transform intraoperative video into clinically meaningful information, including recog-nition of anatomy, instruments, workflow, surgical actions, quality criteria, adverse events, and critical moments. Finally, we outline the major challenges that must be addressed before these systems can deliver routine clinical value, including representa-tive data collection, robust validation, workflow integration, regulation, liability, pri-vacy, and equitable access. Rather than replacing surgical judgment, AI for quality assurance should be understood as a set of tools for augmenting the surgical team, scaling expert review, and helping surgery evolve toward a learning system in which intraoperative care is continuously observed, assessed, and improved.