Artificial Intelligence in Immunology: Predictive Models for Immune System Disorders

The complexity of immunological illnesses makes them difficult to diagnose and treat. Conventional methods mostly concentrate on treating symptoms after these appear, depending on retrospective data and a small number of clinical indicators, which causes diagnoses to be made later and interventions to be less successful. The study presents an AI-powered predictive approach that uses machine learning and deep learning to foresee immune system problems before symptoms appear. The method improves early detection and diagnosis precision by combining large datasets, such as genomic, proteomic, and clinical data. The proposed approach surpassed conventional systems in accuracy, precision, and recall, obtaining 92.5% accuracy and 88.0% sensitivity in early detection. The results show significant improvements over current approaches. The results highlight how the system could transform immunological diagnosis and therapeutic approaches.