Toward Precision Medicine in Celiac Disease: Emerging Roles of Multiomics and Artificial Intelligence

This paper focuses on celiac disease (CD), an autoimmune disorder characterized by small intestinal enteropathy and gastrointestinal symptoms. The prevalence of CD is rising, and there is a need for prompt and accurate diagnosis. The paper discusses the challenges in classifying CD and the consequences of delayed diagnosis, including increased morbidity and mortality rates. It also highlights the Marsh classification, which categorizes CD based on histological characteristics. However, this classification has limitations, which can lead to misdiagnosis. The emergence of multiomics data and artificial intelligence (AI) is presented as a potential solution to improve CD diagnosis and management. Multiomics data integration enables a more precise diagnosis, while AI and machine learning (ML) techniques facilitate the identification of clinically relevant patterns and the development of diagnostic models. According to the findings, the integration of AI and multiomics data holds transformative potential for the diagnosis and management of CD, facilitating early diagnosis and personalized treatment strategies. AI-assisted methods, including video capsule endoscopy (VCE) analysis, have shown sensitivities exceeding 90%, enhancing diagnostic accuracy while reducing invasiveness. As novel biomarkers are identified, individualized management approaches can evolve, leading to improved patient outcomes, though further efforts are needed to standardize these technologies in clinical practice.