Artificial Intelligence for Benign Esophageal Stricture Dilation: An Evidence‐Transferability Framework and Safety‐First Roadmap

ABSTRACT Background Endoscopic dilation is first‐line therapy for symptomatic benign esophageal strictures but remains operator‐dependent. Artificial intelligence (AI) could support assessment, risk stratification, and documentation; however, evidence from adjacent tasks is often mistaken for procedure‐specific validation. Aim To map the evidence for AI in benign esophageal stricture dilation, assess transferability and methodological quality, and propose a translational pathway. Methods We searched PubMed/MEDLINE, Scopus, and Europe PMC from inception through June 28, 2026, using prespecified searches in adults and children. Studies were classified by evidence provenance and appraised using design‐adapted domain considerations. Results Within the search date and eligibility criteria, no AI system was identified that guided, monitored, or assessed dilation itself; two retrospective single‐center studies applied machine learning in the target population for other tasks. Adjacent neoplasia and engineering studies support component feasibility, while clinical cohorts identify candidate predictors. Functional lumen imaging probe technology provides non‐AI measurement and potential reference labels. Recurrent concerns included selected or retrospective samples, limited external validation, and high concern about applicability to dilation. Performance cannot be transferred across mismatched target tasks, acquisition physics, reference standards, populations or operating contexts, or consequences of error. Conclusion Adjacent evidence can justify candidate components. Procedure‐specific calibration and validation remain necessary. Priorities are calibrated morphometric variables with prespecified reference standards and reproducible documentation using synchronized multimodal and outcome data. External validation should be followed by human‐controlled advisory testing before intraprocedural automation.