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Poulos et al. Mini-invasive Surg. 2025;9:6 Mini-invasive Surgery
DOI: 10.20517/2574-1225.2024.42

Review Open Access

The current state of artificial intelligence in robotic

esophageal surgery

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Constantine M. Poulos , Ryan Cassidy , Eamon Khatibifar , Erik Holzwanger , Lana Schumacher 1
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Department of Surgery, Tufts Medical Center, Tufts School of Medicine, Boston, MA 02116, USA.
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Center for Advanced Endoscopy, Department of Gastroenterology and Hepatology, Tufts Medical Center, Tufts School of
Medicine, Boston, MA 02116, USA.
Correspondence to: Dr. Lana Schumacher, Department of Surgery, Tufts Medical Center, Tufts School of Medicine, 800
Washington Avenue, Boston, MA 02116, USA. E-mail: Lana.Schumacher@tuftsmedicine.org
How to cite this article: Poulos CM, Cassidy R, Khatibifar E, Holzwanger E, Schumacher L. The current state of artificial
intelligence in robotic esophageal surgery. Mini-invasive Surg. 2025;9:6. https://dx.doi.org/10.20517/2574-1225.2024.42
Received: 10 May 2024 First Decision: 21 Nov 2024 Revised: 23 Dec 2024 Accepted: 5 Feb 2025 Published: 12 Feb 2025

Academic Editors: Farid Gharagozloo, Itasu Ninomiya Copy Editor: Ting-Ting Hu Production Editor: Ting-Ting Hu

Abstract
Artificial intelligence (AI) is becoming increasingly utilized as a tool for physicians to optimize medical care and
patient outcomes. The multifaceted approach to managing esophageal cancer provides a perfect opportunity for
machine learning to support clinicians in all stages of management. Preoperatively, AI may aid gastroenterologists
and surgeons in diagnosing and prognosticating premalignant or early-stage lesions. Intraoperatively, AI may also
aid surgeons in identifying anatomic structures or minimize the learning curve for new learners. Postoperatively,
machine learning algorithms can help predict complications and guide high-risk patients through recovery. While
still evolving, AI holds promise in enhancing the efficiency and efficacy of multidisciplinary esophageal cancer care.

Keywords: Artificial intelligence, machine learning, neural network, esophagectomy, robotic

INTRODUCTION
The treatment of esophageal malignancy is complex and requires coordination among a multidisciplinary
team including gastroenterologists, medical and radiation oncologists, and thoracic surgeons. Similar to its
applications in lung cancer, artificial intelligence (AI) and artificial neural networks (ANNs) are increasingly
used to guide clinicians in all stages of esophageal cancer management, from diagnosis to non-operative
management and surgical intervention . The algorithms are trained on large amounts of clinical data and
[1-4]

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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