Advanced immunotherapy across diseases and the role of artificial intelligence: A review

Immunotherapy, a therapeutic strategy aimed at modulating the host immune system, has undergone rapid evolution over recent decades, particularly in oncology. Advanced methodologies, including immune checkpoint inhibition, cytokine therapy, chimeric antigen receptor T-cell therapy (CAR-T), and tumor-infiltrating lymphocyte therapies, have significantly transformed cancer treatment. This review summarizes recent advancements in immunotherapy and examines its expanding applications across a range of diseases, such as autoimmune disorders, infectious diseases, transplant rejection, and allergic conditions. A structured literature search was conducted using PubMed and Google Scholar, prioritizing studies published from 2015 to 2026. The findings underscore the efficacy of monoclonal antibodies, adoptive cell therapies, cytokine modulation, and checkpoint-targeted strategies beyond oncology. However, challenges remain, including variable patient responses, immune-related adverse events, and treatment costs. This review also explores the emerging role of artificial intelligence (AI) in enhancing personalized immunotherapy through patient stratification, biomarker identification, and predictive modeling. The integration of multi-omics data with AI presents promising opportunities for improving treatment efficacy and safety, although issues related to data quality, interpretability, regulatory frameworks, and ethical considerations must be addressed. In conclusion, immunotherapy is rapidly extending beyond cancer, and AI-supported personalized approaches offer a promising pathway to safer, more effective, and broadly applicable treatments.