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Background <p>Artificial intelligence (AI) has seen considerable growth mainly in surgical oncology, with current applications primarily centered on cancer diagnosis, staging, treatment planning, and outcomes prediction. Aim of the present scoping review was to describe the actual evidence and future perspectives of AI-application in the field of benign esophageal diseases.</p> Methods <p>This scoping review summarizes current evidence on AI utilization in the diagnosis and surgical management of esophageal benign disease such as achalasia, Barrett’s esophagus, gastroesophageal reflux disease (GERD), hiatus hernia (HH), and Zenker diverticulum. PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar databases were searched until November 2025.</p> Results <p>Overall, 37 studies published were included. The integration of AI within the surgical protocols of tertiary referral centers may offer the potential to enhance multidisciplinary decision-making, provide intraoperative assistance, and lead to improved patient outcomes by personalizing treatment of reflux disease, motility disorders and esophageal diverticula. Also, there is an urgent need of responsible AI development and implementation to support surgical education through objective skill assessment, simulation-based training, and competency evaluation. Machine learning, deep learning and hybrid models are still underexplored. Since continuous learning and system adaptability are crucial in healthcare, collaborative efforts to develop robust and validated patient-centered AI tools that align with real-world surgical workflow have the potential to uncover hidden trends and to deliver reliable predictions. Ultimately, AI applications within esophageal surgery must adhere to the ethical standards that define surgical practice: safety, transparency, accountability, equity, and dedication to patient welfare. By ensuring that innovation remains aligned with these foundational principles, AI can serve to elevate both the precision of surgical care and the preparation of future surgeons.</p> Conclusions <p>AI can improve every stage of surgical care for benign esophageal disease, from diagnosis to postoperative management. It may also help standardize surgeon training and speed up learning for laparoscopic and robotic procedures. Realizing AI’s full benefits will require strong research, ethical practices, and thorough surgeon education.</p>

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Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月20日

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  • Alberto Aiolfi,
  • Quan Wang,
  • Pietro Mascagni,
  • Davide Bona,
  • Nicola Leone,
  • Luigi Bonavina

摘要

Background

Artificial intelligence (AI) has seen considerable growth mainly in surgical oncology, with current applications primarily centered on cancer diagnosis, staging, treatment planning, and outcomes prediction. Aim of the present scoping review was to describe the actual evidence and future perspectives of AI-application in the field of benign esophageal diseases.

Methods

This scoping review summarizes current evidence on AI utilization in the diagnosis and surgical management of esophageal benign disease such as achalasia, Barrett’s esophagus, gastroesophageal reflux disease (GERD), hiatus hernia (HH), and Zenker diverticulum. PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar databases were searched until November 2025.

Results

Overall, 37 studies published were included. The integration of AI within the surgical protocols of tertiary referral centers may offer the potential to enhance multidisciplinary decision-making, provide intraoperative assistance, and lead to improved patient outcomes by personalizing treatment of reflux disease, motility disorders and esophageal diverticula. Also, there is an urgent need of responsible AI development and implementation to support surgical education through objective skill assessment, simulation-based training, and competency evaluation. Machine learning, deep learning and hybrid models are still underexplored. Since continuous learning and system adaptability are crucial in healthcare, collaborative efforts to develop robust and validated patient-centered AI tools that align with real-world surgical workflow have the potential to uncover hidden trends and to deliver reliable predictions. Ultimately, AI applications within esophageal surgery must adhere to the ethical standards that define surgical practice: safety, transparency, accountability, equity, and dedication to patient welfare. By ensuring that innovation remains aligned with these foundational principles, AI can serve to elevate both the precision of surgical care and the preparation of future surgeons.

Conclusions

AI can improve every stage of surgical care for benign esophageal disease, from diagnosis to postoperative management. It may also help standardize surgeon training and speed up learning for laparoscopic and robotic procedures. Realizing AI’s full benefits will require strong research, ethical practices, and thorough surgeon education.