Esophageal Intelligence: Implementing Artificial Intelligence Into the Diagnostics of Esophageal Motility and Impedance <scp>pH</scp> Monitoring

Esophageal motility disorders (EMDs) encompass a range of functional abnormalities, including achalasia, ineffective esophageal motility (IEM), esophagogastric junction outflow obstruction (EGJOO), and distal esophageal spasm (DES). Diagnostic modalities like high-resolution esophageal manometry (HREM), Functional Lumen Imaging Probe (FLIP), and impedance analysis are invaluable but often limited by interpretive variability and the need for expert analysis. Artificial intelligence (AI) has emerged as a transformative tool in addressing these challenges. This manuscript explores the integration of AI in EMD diagnostics, showcasing its ability to enhance diagnostic accuracy, optimize workflows, and standardize interpretation across centers. Advanced algorithms, including convolutional neural networks (CNNs) and machine learning (ML) models, achieve high accuracy in automating classifications, subtyping disorders like achalasia, and improving diagnostic consistency. Furthermore, AI's predictive capabilities extend to treatment outcome modeling, enabling personalized care strategies and longitudinal tracking. AI also offers significant potential in medical education by reducing learning curves and standardizing esophageal motility interpretation training. These advancements collectively emphasize the role of AI in revolutionizing EMD diagnosis, treatment, and training, promising improved patient outcomes and broader clinical utility.