Artificial Intelligence for Diagnosis of Esophageal Manometry: A Narrative Review

Abstract

PURPOSE OF REVIEW: High-resolution manometry (HRM), the gold standard for diagnosing esophageal motility disorders, remains constrained by inter-rater variability, limited access to expert esophagologists, and inadequate training infrastructure. Many gastroenterology fellowship programs provide insufficient motility training, and dedicated expert centers remain geographically concentrated. Emerging Artificial intelligence (AI) tools have the potential to automate diagnosis, and augment clinician interpretive capacity and democratizing expert-level motility assessment. RECENT FINDINGS: Twenty-two studies encompassing over 5,000 patients were synthesized across three overlapping developmental phases: early machine learning for feature extraction and classification; deep learning deployed toward automated pattern recognition and motility classification and emerging multimodal and large language model-based frameworks augmenting clinical interpretation and decision-making, with diagnostic accuracies ranging from 71 to 97%. AI integration of multimodal manometric data may reveal pressure signatures imperceptible to human visual inspection, pointing toward phenotypes beyond the Chicago Classification. No study has yet demonstrated improved patient outcomes. AI's role in motility training remains largely unexplored. The AI-clinician partnership represents the most promising trajectory for esophageal manometry interpretation in coming years. AI-augmented interpretation has the potential to eliminate the two-tier diagnostic gap between academic and community practice, to accelerate trainee competency through scalable AI-supervised case libraries, and to uncover physiologic phenotypes beyond human interpretation.