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Artificial Intelligence in Esophagectomy: A Systematic Review.

Vladimir Aleksiev1,2, Daniel Markov3,4, Kristian Bechev5

  • 1Department of Cardiovascular Surgery, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.

Journal of Clinical Medicine
|March 28, 2026
PubMed
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This summary is machine-generated.

Artificial intelligence (AI) shows promise in improving intraoperative visualization and safety during esophagectomy by analyzing surgical videos. However, current research is preliminary, requiring more robust studies before widespread clinical adoption for this complex oncologic procedure.

Area of Science:

  • Surgical Oncology
  • Medical Artificial Intelligence
  • Minimally Invasive Surgery

Background:

  • Esophagectomy is a complex oncologic surgery with significant morbidity.
  • Intraoperative visualization limitations can lead to complications like nerve injury and bleeding.
  • Artificial intelligence (AI) offers potential to enhance surgical perception and safety.

Purpose of the Study:

  • To systematically review the application of AI-based intraoperative video analysis in esophagectomy.
  • To assess the current state of AI technology in enhancing surgical perception and safety during this procedure.

Main Methods:

  • Systematic review following PRISMA guidelines.
  • Searched PubMed, Scopus, and Web of Science for studies up to January 2026.
Keywords:
artificial intelligenceesophagectomyintraoperative video analysisminimally invasive surgeryrobotic surgery

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  • Included human studies of any surgical approach, assessing AI tasks, methodology, validation, performance, and outcomes.

    • Main Results:

    • Six studies evaluated AI in minimally invasive or robotic esophagectomy.
    • AI applications included anatomical recognition, nerve injury detection, and phase identification.
    • AI performance was comparable to experts for specific tasks, with real-time capabilities.

    Conclusions:

    • AI-based intraoperative analysis is feasible for esophagectomy, potentially improving anatomical recognition and risk detection.
    • Current evidence is preliminary and heterogeneous, necessitating prospective, multicenter validation.
    • AI should be considered a complementary tool, not a standalone decision system, pending further research.