AI-Driven Biomarker Discovery and Personalized Allergy Treatment: Utilizing Machine Learning and NGS.

PURPOSE OF REVIEW

This review explores the transformative potential of artificial intelligence (AI) and next-generation sequencing (NGS) in allergy diagnostics and treatment. It focuses on leveraging these technologies to enhance precision in biomarker discovery, patient stratification, and personalized management strategies for allergic diseases. RECENT FINDINGS: AI-driven algorithms, particularly machine learning and deep learning, have enabled the identification of complex molecular patterns and predictive markers in allergies, such as IgE levels and cytokine profiles. Integration with NGS techniques, including single-cell RNA sequencing, has uncovered unique immune response signatures, providing insights into molecular mechanisms driving allergic reactions. These innovations have advanced diagnostic accuracy, treatment personalization, and real-time monitoring capabilities, especially in allergen immunotherapy. Combining AI and NGS technologies represents a paradigm shift in allergy research and clinical practice. These advancements facilitate precision diagnostics and personalized treatments, ensuring safer and more effective interventions tailored to individual patient profiles. Despite data integration and clinical implementation challenges, these technologies promise improved outcomes and quality of life for allergy sufferers.