Artificial Intelligence in Peroral Endoscopic Myotomy: Current Evidence, Limitations, and Future Directions

AI in POEM

Abstract

Peroral endoscopic myotomy (POEM) is an established treatment for achalasia and selected spastic esophageal motility disorders, but several aspects of care remain highly operator-dependent. These include early diagnosis, patient selection, procedural planning, myotomy length, intra-procedural safety, training assessment, and prediction of post-procedure reflux. Artificial intelligence (AI) may help address these limitations by integrating endoscopic video, high-resolution manometry, timed barium esophagram, EndoFLIP, clinical variables, procedural data, and follow-up outcomes into more personalized decision-support tools. This narrative review critically examines the evidence for AI across the POEM pathway and distinguishes direct achalasia or POEM evidence from adjacent and conceptual applications. Early AI applications are most likely to emerge in achalasia detection during diagnostic endoscopy, automated motility interpretation, video-based procedural assessment, and prediction of clinical response or post-POEM reflux. More ambitious applications, such as real-time tunnel orientation, vessel recognition, mucosal injury alerts, and personalized myotomy planning, remain largely investigational. Most AI-specific POEM applications remain at an early or conceptual stage and require multicenter validation, standardized outcomes, and careful clinical governance. AI should support, rather than replace, expert judgment, and an AI-derived reflux estimate cannot replace standard follow-up or objective reflux assessment.