ArticleLangenbeck's archives of surgery2026
Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.
Article in Langenbeck's archives of surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Role of liquid biopsy in the multimodal assessment and treatment of esophageal cancer: a surgical perspective.Updates in surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
methodsThis 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.
resultsOverall, 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.
conclusionsAI 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.