ReviewSurgical endoscopy2026
Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.
Review in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAbdominal wall hernia repair is amongst the highest-volume operations in general surgery, yet recurrence, mesh-related morbidity, and surgeon-dependent variability remain unsolved. Artificial intelligence (AI) is increasingly applied across the perioperative continuum of hernia care.
methodsWe performed a narrative synthesis of PubMed/MEDLINE, Web of Science, and Scopus from database inception to 31 December 2025, with a supplementary verification search on 15 January 2026 and eligibility based on the earliest online (Online-First/Epub) date. Studies were selected thematically rather than systematically for a clinically oriented synthesis. Two reviewers with clinical and data-science backgrounds screened independently; full-text agreement was substantial (Cohen κ = 0.82). Reporting followed the Scale for the Assessment of Narrative Review Articles (SANRA; six items scored 0-2, maximum 12; author self-assessment 10/12). KEY
findingsAI applications cluster into four domains: preoperative risk stratification and CT-based body composition analysis; intraoperative computer vision for phase recognition and critical structure detection; postoperative outcome prediction and digital follow-up; and registry-based big data analytics. Preoperative machine learning risk modeling and CT-based sarcopenia quantification are closest to clinical translation, although the evidence remains predominantly retrospective and internally validated; real-time intraoperative landmark detection, augmented reality, and federated learning remain at earlier translational stages.
conclusionsHernia surgery is well suited for AI integration because of its standardized anatomy, repeatable workflows, and growing registry and video repositories. The distinct contribution of this review is a criteria-based clinical readiness framework and a guideline-anchored translational roadmap, rather than a re-cataloguing of existing studies. Clinical adoption now depends on prospective external validation, demonstration of patient-level outcome benefit, adherence to TRIPOD+AI, CONSORT-AI, and SPIRIT-AI standards, and clarification of regulatory pathways for AI as a software medical device.
Indexed as
Identifiers
42791408What OpenQuestion 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 OpenQuestion graph.