Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.

BACKGROUND

Abdominal 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.

METHODS

We 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 FINDINGS: AI 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.

CONCLUSIONS

Hernia 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.