AI for Quality Assurance in the Operating Room
- URL: http://arxiv.org/abs/2606.30657v1
- Date: Tue, 16 Jun 2026 09:05:18 GMT
- Title: AI for Quality Assurance in the Operating Room
- Abstract summary: 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.<n>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.
- Score: 7.402717868707822
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery [5.586191108738563]
We present the world's largest dataset of cataract surgery videos, comprising 2,000 recordings.<n>We propose an AI-powered analytical framework that employs advanced computer vision and signal-processing techniques to automatically evaluate surgical videos.<n>A significant advantage of our framework over previous methods lies precisely in its explainability of outputs, elevating it beyond merely an opaque skill classification tool.
arXiv Detail & Related papers (2026-08-18T08:45:27Z) - A Multi-Agent LLM Framework for Rating the Quality of Surgical Feedback [52.053200111780626]
Verbal feedback delivered by attending surgeons in the operating room plays a critical formative role in resident skill acquisition.<n>We introduce a two-stage LLM-based framework that discovers interpretable feedback quality criteria grounded in the context of surgical training.<n>Our method uses multi-agent and surgical domain knowledge injection to discover a small set of human interpretable scoring criteria.
arXiv Detail & Related papers (2026-05-25T05:31:44Z) - Kinematic-Based Assessment of Surgical Actions in Microanastomosis [4.92174988745803]
We introduce an AI-driven framework for automated action segmentation and performance assessment in microanastomosis procedures.<n>A dataset of 58 expert-rated microanastomosis videos demonstrates the effectiveness of our approach.
arXiv Detail & Related papers (2025-12-30T02:18:49Z) - Surgeons vs. Computer Vision: A comparative analysis on surgical phase recognition capabilities [65.66373425605278]
Automated Surgical Phase Recognition (SPR) uses Artificial Intelligence (AI) to segment the surgical workflow into its key events.<n>Previous research has focused on short and linear surgical procedures and has not explored if temporal context influences experts' ability to better classify surgical phases.<n>This research addresses these gaps, focusing on Robot-Assisted Partial Nephrectomy (RAPN) as a highly non-linear procedure.
arXiv Detail & Related papers (2025-04-26T15:37:22Z) - Hypergraph-Transformer (HGT) for Interactive Event Prediction in Laparoscopic and Robotic Surgery [47.47211257890948]
We propose a predictive neural network that is capable of understanding and predicting critical interactive aspects of surgical workflow from intra-abdominal video.<n>We verify our approach on established surgical datasets and applications, including the detection and prediction of action triplets.<n>Our results demonstrate the superiority of our approach compared to unstructured alternatives.
arXiv Detail & Related papers (2024-02-03T00:58:05Z) - ST(OR)2: Spatio-Temporal Object Level Reasoning for Activity Recognition
in the Operating Room [6.132617753806978]
We propose a new sample-efficient and object-based approach for surgical activity recognition in the OR.
Our method focuses on the geometric arrangements between clinicians and surgical devices, thus utilizing the significant object interaction dynamics in the OR.
arXiv Detail & Related papers (2023-12-19T15:33:57Z) - Prediction of Post-Operative Renal and Pulmonary Complications Using
Transformers [69.81176740997175]
We evaluate the performance of transformer-based models in predicting postoperative acute renal failure, pulmonary complications, and postoperative in-hospital mortality.
Our results demonstrate that transformer-based models can achieve superior performance in predicting postoperative complications and outperform traditional machine learning models.
arXiv Detail & Related papers (2023-06-01T14:08:05Z) - Quantification of Robotic Surgeries with Vision-Based Deep Learning [45.165919577877695]
We propose a unified deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery.
We validated our framework on four video-based datasets of two commonly-encountered types of steps within minimally-invasive robotic surgeries.
arXiv Detail & Related papers (2022-05-06T06:08:35Z) - CholecTriplet2021: A benchmark challenge for surgical action triplet
recognition [66.51610049869393]
This paper presents CholecTriplet 2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos.
We present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
A total of 4 baseline methods and 19 new deep learning algorithms are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%.
arXiv Detail & Related papers (2022-04-10T18:51:55Z) - Real-time Informative Surgical Skill Assessment with Gaussian Process
Learning [12.019641896240245]
This work presents a novel Gaussian Process Learning-based automatic objective surgical skill assessment method for ESSBSs.
The proposed method projects the instrument movements into the endoscope coordinate to reduce the data dimensionality.
The experimental results show that the proposed method reaches 100% prediction precision for complete surgical procedures and 90% precision for real-time prediction assessment.
arXiv Detail & Related papers (2021-12-05T15:35:40Z) - Towards Unified Surgical Skill Assessment [18.601526803020885]
We propose a unified multi-path framework for automatic surgical skill assessment.
We conduct experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries.
arXiv Detail & Related papers (2021-06-02T09:06:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.