Recent Advances in Machine Learning for Esophageal Disorders

This document is a review article that discusses the application of machine learning (ML) in the diagnosis and treatment of esophageal disorders. It outlines the challenges faced in clinical practice due to the complex pathophysiology and diverse manifestations of these disorders, which complicate early diagnosis and risk stratification. The review synthesizes various studies that demonstrate the superior performance of ML models in diagnosing conditions such as gastroesophageal reflux disease, Barrett’s esophagus, and esophageal cancer. It highlights accuracy rates ranging from 80% to 95% for different diagnostic approaches. Additionally, the article addresses the challenges of data standardization and model interpretability while suggesting that emerging technologies like federated learning and explainable artificial intelligence could enhance clinical integration. The findings indicate that ML has the potential to significantly improve the management of esophageal diseases, paving the way for advancements in precision medicine.