ReviewSurgical endoscopy2025
Artificial intelligence for intraoperative video analysis in robotic-assisted esophagectomy.
Review in Surgical endoscopy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in esophageal surgery: a systematic review.Journal of robotic surgery · 2025Pooled it
- Decoding surgical competence: surgical gesture analysis can determine competence in simulated VATS lobectomy, a validation study.Surgical endoscopy · 2026Article
- Review
- Mapping the evolution of deep learning and computer vision in robotic surgery: a bibliometric analysis of surgical video intelligence, instrument perception, and clinical translation.Journal of robotic surgery · 2026Review
- Are we developing the right intraoperative AI assistance? Surgeons' perspectives and desired functions.Surgical endoscopy · 2026Article
- Review
- Education and training of surgical residents in upper gastrointestinal surgery: a European survey.Updates in surgery · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Opportunities and obstacles in harnessing intraoperative data.Journal of abdominal wall surgery : JAWS · 2026Article
- Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.Healthcare (Basel, Switzerland) · 2025Review
- Improving surgical phase recognition using self-supervised deep learning.Scientific reports · 2025Article
- Robotic Heller Myotomy for Achalasia: A Narrative Review of the State-of-the-Art Technique and Its Future Directions.Cureus · 2025Review
- From Data to Decision: A Comprehensive Review of Real-Time Analytics and Smart Technologies in the Surgical Suite.Methodist DeBakey cardiovascular journal · 2025Review
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRobotic-assisted minimally invasive esophagectomy (RAMIE) is a complex surgical procedure for treating esophageal cancer. Artificial intelligence (AI) is an uprising technology with increasing applications in the surgical field. This scoping review aimed to assess the current AI applications in RAMIE, with a focus on intraoperative video analysis.
methodsTo identify all articles utilizing AI in RAMIE, a comprehensive literature search was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis for scoping reviews of Medline and Embase databases and the Cochrane Library. Two independent reviewers assessed articles for quality and inclusion.
resultsOne hundred and seventeen articles were identified, of which four were included in the final analysis. Results demonstrated that the main AI applications in RAMIE were intraoperative video assessment and the evaluation of surgical technical skills to evaluate surgical performance. AI was also used for surgical phase recognition to support clinical decision-making through intraoperative guidance and identify key anatomical landmarks. Various deep-learning networks were used to generate AI models, and there was a strong emphasis on using high-quality standardized video frames.
conclusionsThe use of AI in RAMIE, especially in intraoperative video analysis and surgical phase recognition, is still a relatively new field that should be further explored. The advantages of using AI algorithms to evaluate intraoperative videos in an automated manner may be harnessed to improve technical performance and intraoperative decision-making, achieve a higher quality of surgery, and improve postoperative outcomes.
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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 OpenQuestion graph.