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Component-resolved diagnosis (CRD) has emerged as a valuable complement to conventional allergy testing by identifying specific allergenic molecules rather than whole allergen extracts. For otorhinolaryngologists managing patients with allergic rhinitis, polysensitization, oral allergy syndrome, or uncertain clinical–test correlations, CRD can provide a more precise understanding of the patient’s sensitization profile.

This instructional course will introduce the basic principles of molecular allergology and demonstrate how CRD can be incorporated into everyday rhinology practice. Particular emphasis will be placed on distinguishing genuine primary sensitization from cross-reactivity, recognizing major allergen components and panallergens, and interpreting sensitization to profilins, polcalcins, lipid transfer proteins, tropomyosins, and cross-reactive carbohydrate determinants. Clinically relevant examples involving grass, tree and weed pollens, house dust mites, moulds, and selected food allergens will be discussed.

Participants will learn how molecular findings may clarify apparently conflicting skin-prick test or specific IgE results, identify the allergen sources most likely to be responsible for symptoms, and improve the selection of allergens for allergen immunotherapy. The course will also address the limitations of CRD, including the distinction between sensitization and clinically relevant allergy, the need to interpret results within the context of exposure and symptom history, and situations in which molecular testing is unlikely to alter management.

Through case-based discussions, the course aims to provide otorhinolaryngologists with a practical framework for requesting, interpreting, and applying CRD appropriately. At the end of the session, participants should be able to use molecular allergy testing more confidently while avoiding both overdiagnosis and unnecessary allergen immunotherapy.

Allergy immunotherapy is currently the only disease-modifying treatment available for allergic diseae. It’s effectiveness for airborne and food allergies has been well-proven, yet it still remains underutilized and poorly-understood. This didactic lecture will introduce the learner to immunotherapy, review the benefits and risks, discuss patient selection, and clarify the immunologic mechanisms that are taking place over the course of therapy.

The integration of Artificial Intelligence (AI) into allergy and immunology is redefining how clinicians diagnose, treat, and monitor immune-mediated diseases. As the global prevalence of allergic conditions continues to rise—including asthma, food allergies, environmental allergies, and atopic dermatitis—there is increasing need for tools that improve diagnostic accuracy, enhance treatment personalization, and support continuous patient management. AI, through machine learning (ML) and deep learning (DL), offers powerful solutions by analyzing complex, multidimensional datasets that extend beyond traditional clinical evaluation. These include electronic health records, genomics and proteomics, environmental exposures, imaging, wearable device outputs, and real-time physiologic data. By integrating these data streams, AI systems can identify subtle patterns and predictive signals that inform early diagnosis and disease stratification.

In clinical diagnostics, ML algorithms have demonstrated superior performance over conventional methods by accurately predicting asthma and atopic dermatitis risk, distinguishing allergic from non-allergic phenotypes, and supporting earlier intervention. AI-based models continuously update risk assessments as new data become available, supporting dynamic clinical decision-making. Moreover, DL tools can analyze heterogeneous patient presentations and facilitate more precise phenotyping in complex allergic disorders.

AI is also advancing precision medicine through optimization of allergen-specific immunotherapy (AIT). By analyzing immune biomarkers, multi-omics data, and longitudinal patient responses, AI can predict which patients are likely to benefit from AIT, determine optimal dosing strategies, and reduce the risk of adverse reactions. This level of personalization enhances treatment efficacy and adherence—challenges that have historically limited the full potential of immunotherapy.

In drug discovery, AI accelerates the identification of new therapeutic targets and biomarkers for allergic and autoimmune diseases. In silico screening, neural network–based molecular modeling, and automated compound analysis substantially reduce research timelines. These tools are particularly valuable for complex conditions such as lupus and rheumatoid arthritis, where traditional biomarker discovery processes have been slow and resource-intensive.

AI-enhanced wearable devices are further transforming patient management by enabling continuous monitoring of physiologic markers and environmental triggers. Smart inhalers, respiratory sensors, and smartwatch-based biometrics provide real-time data on lung function, inflammation, medication adherence, and exposure to allergens and pollutants. When combined with AI-driven forecasting tools for pollen, air quality, and weather-related triggers, these systems allow patients to take proactive steps and clinicians to make informed adjustments to treatment plans. This shift enables anticipatory management rather than reactive care.

Despite these promising developments, significant challenges remain. Data privacy concerns, algorithmic bias, limited dataset diversity, and difficulties integrating AI into established clinical workflows are ongoing barriers. Addressing these issues will require stronger regulatory frameworks, ethical oversight, and interdisciplinary collaboration between clinicians, data scientists, and technology developers.

Overall, AI represents a pivotal advancement for allergy and immunology, offering more predictive, personalized, and proactive approaches to care. As these technologies mature, they have the potential to significantly improve patient outcomes and advance the future of immune-mediated disease management.