Deep learning assisting the surgical management of gynecologic cancers - Review

Sep 23, 2026

This review examines the current role of deep learning in the surgical management of gynecologic cancers. It evaluates applications across surgical training, intraoperative guidance, and outcome prediction, highlighting how models can analyze high-dimensional data from medical images, videos, and clinical variables. The review also details limitations that impede translation into routine practice, including reliance on retrospective evidence, limited datasets, and insufficient external validation. It concludes that AI may standardize and personalize care while leaving gaps in implementation.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages6

Reading Time15 min

AccessFree to download

Purpose of review

Recent findings

Summary

Introduction

Artificial intelligence in surgical management of gynecological cancers: enhancing surgical skills acquisition

Key points

REVIEW UPRI RNIENTON Deep learning assisting the surgical management of gynecologic cancers Veronica Tiusa, Martina Arcieria, Stefano Restainoa,* and Giuseppe Vizziellia,b,* Purpose of review This review examines the current role of deep learning in the surgical management of gynecologic cancers. It aims to evaluate applications across surgical training, intraoperative guidance, and outcome prediction, while identifying limitations that hinder translation into routine clinical practice. Recent findings Deep learning-based systems show promising performance in enhancing surgical skills through objective assessment and simulation platforms. Intraoperatively, deep learning models can recognize anatomical structures, surgical phases, and tumor tissue, with high diagnostic accuracy in selected settings. Emerging techniques such as surgical optomics and hyperspectral imaging may further improve tumor detection. Additionally, predictive models integrating clinical, imaging, and intraoperative data demonstrate potential in estimating complications, resource utilization, and survival outcomes. However, most studies are retrospective, based on limited datasets, and lack external validation, with minimal evidence of real-world clinical impact. Summary Artificial intelligence, particularly deep learning algorithms, represents a rapidly evolving tool in gynecologic oncologic surgery, with potential to standardize and personalize care. Nevertheless, significant gaps remain between experimental performance and clinical implementation. Keywords artificial intelligence, deep learning, gynecology, oncology, surgery INTRODUCTION Surgical management of gynecologic cancers remains one of the most complex areas in oncologic care, requiring the integration of technical skill, anatomical understanding, real-time decision-making, and accurate interpretation of tumor biology. Variability in surgical performance, intraoperative judgment, and patient selection continues to represent a major challenge, directly impacting oncologic outcomes and perioperative morbidity. Artificial intelligence (AI), particularly deep learning (DL), has emerged as a promising tool to address these limitations by enabling the analysis of high-dimensional data such as medical images, surgical videos, and clinical variables. DL-based models have shown increasing capabilities in pattern recognition and predictive modeling, suggesting applications across the entire surgical pathway – from training and preoperative planning to intraoperative guidance and postoperative outcome prediction. Within gynecologic oncology, AI integration into surgical practice is still in its early stages but is rapidly evolving [1&] . Current research focuses on three main domains: enhancement of surgical training, intraoperative assistance through real-time recognition of anatomical structures and tumor tissue, and prediction of surgical and oncologic outcomes through multimodal data integration. These developments may support a more standardized, data-driven surgical approach, potentially reducing variability and improving patient-specific decisionmaking. However, despite the growing body of evidence and promising performance metrics, substantial gaps remain between experimental development and aClinic of Obstetrics and Gynecology, “S. Maria della Misericordia”University Hospital, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC) and bMedical Area Department (DAME), in Department of Medicine (DMED), University of Udine, Udine, Italy Correspondence to Giuseppe Vizzielli, PhD, Medical Area Department (DAME), Department of Medicine (DMED), University of Udine, piazzale S. Maria della Misericordia 15, Udine 33100, Italy. Tel: +33 432559695; e-mail: [giuseppe.vizzielli@uniud.it](giuseppe.vizzielli@uniud.it) *Stefano Restaino, Giuseppe Vizzielli: co-last authorship. Curr Opin Oncol 2026, 38:406–411 DOI:10.1097/CCO.0000000000001254 This is an open access ar

FAQs

What is the purpose of this review on deep learning and gynecologic cancer surgery?

The review examines the current role of deep learning in surgical management of gynecologic cancers and evaluates applications across training, intraoperative guidance, and outcome prediction, while identifying barriers to routine clinical adoption.

How can deep learning support intraoperative surgical guidance?

Deep learning models can recognize anatomical structures, surgical phases, and tumor tissue with high diagnostic accuracy in selected settings, and emerging imaging techniques may further improve tumor detection.

What limitations currently hinder real-world clinical implementation?

Many studies are retrospective, use limited datasets, and lack external validation, with additional challenges related to interpretability, reproducibility, and ethical responsibility.

Sep 23, 2026

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