Can artificial intelligence improve the diagnosis and management of patients with eosinophilic esophagitis?

Eosinophilic esophagitis (EoE) is a chronic, immune-mediated condition leading to esophageal inflammation and a range of symptomatic complications if inadequately managed. Recent epidemiological trends indicate a significant increase in EoE prevalence, complicating patient care amid diagnostic challenges associated with conventional methods such as endoscopy and histopathological analysis. This review explores the promise of artificial intelligence (AI) and deep learning models in enhancing the diagnosis and management of EoE, addressing the limitations of traditional approaches including inter-observer variability, invasiveness, and delays in diagnosis. By synthesizing findings from peer-reviewed studies, we demonstrate that AI algorithms exhibit high diagnostic accuracy in recognizing subtle endoscopic features and quantifying eosinophilic tissue infiltration. Moreover, these technologies can streamline workflows, reduce dependency on manual assessments, and enhance personalized care strategies. Despite the potential benefits, challenges regarding the integration of AI into clinical practice remain, including issues of algorithmic bias, data privacy, and the need for robust validation across diverse healthcare settings. Future research should focus on multicenter studies to confirm AI's effectiveness, explore non-invasive diagnostic alternatives, and promote the ethical application of AI to optimize patient outcomes in EoE. This review highlights AI's transformative capacity to reshape the diagnostic landscape of EoE, underscoring the requirement for ongoing evaluation and collaboration among clinicians, researchers, and technology developers to realize its full potential within the healthcare framework.