The current state of artificial intelligence in robotic esophageal surgery

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.

Authors

Institutions

Publication Details

Journal
Mini-invasive Surgery
Published
2025-02-12
DOI
https://doi.org/10.20517/2574-1225.2024.42
Citations
3
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.08

Funders

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article

The current state of artificial intelligence in robotic esophageal surgery

Christine Poulos, Ryan J. Cassidy, Erik Holzwanger, Lana Schumacher et al.
3 citations
Mini-invasive Surgery
Radiomics and Machine Learning in Medical Imaging
4.08
article

The current state of artificial intelligence in robotic esophageal surgery

Christine Poulos, Ryan J. Cassidy, Erik Holzwanger, Lana Schumacher, Eamon Khatibifar
article en
3 citations

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.

Mini-invasive Surgery
Tufts University (US), Tufts Medical Center (US)
Tufts Medical Center
Openalex Percentile: Top 8%
Radiomics and Machine Learning in Medical Imaging
4.08
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