Abstract
Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will allow for better understanding of the diseases, which will enhance clinical management and benefit society and affected individuals and families. Artificial intelligence (AI) is a promising tool in this context, enabling discovery of meaningful patterns in complex data. Numerous studies within pediatric allergy have and continue to use AI, primarily to characterize disease endotypes/phenotypes and to develop models to predict future disease outcomes. However, most implementations have used relatively simplistic data from one source, such as questionnaires. In addition, methodological approaches and reporting are lacking. This review provides a practical hands-on guide for conducting AI-based studies in pediatric allergy, including (1) an introduction to essential AI concepts and techniques, (2) a blueprint for structuring analysis pipelines (from selection of variables to interpretation of results), and (3) an overview of common pitfalls and remedies. Furthermore, the state-of-the art in the implementation of AI in pediatric allergy research, as well as implications and future perspectives are discussed.
Conclusion: AI-based solutions will undoubtedly transform pediatric allergy research, as showcased by promising findings and innovative technical solutions, but to fully harness the potential, methodologically robust implementation of more advanced techniques on richer data will be needed.
Key Points
- AI Potential: AI enables pattern recognition in complex data, which is critical for understanding heterogeneous pediatric allergic diseases.
- Disease Complexity: Pediatric allergies exhibit complex interrelations influenced by genetic and environmental factors, making them suitable for AI-driven studies.
- Applications: AI has been used to identify disease endotypes and develop predictive models, though these are often limited to single-source data like questionnaires.
- Current Gaps: Advanced AI techniques (e.g., deep learning) and integration of multi-omics or unstructured data are lacking in pediatric allergy research.
- Methodological Challenges: Inadequate reporting on computational approaches and poor generalizability limit the impact of AI studies.
- Research Blueprint: The review provides a structured approach to AI research, from data preprocessing and model selection to result interpretation.
- Collaborative Efforts: Multi-center collaborations and multidisciplinary teams are recommended to enhance the robustness and applicability of findings.
- Ethical Considerations: Addressing bias, privacy, and explainability is critical to ensuring ethical and effective AI application in healthcare.
- Promising Innovations: Emerging AI tools offer potential for personalized interventions and improved disease management strategies in pediatrics.
- Future Directions: To fully harness AI’s potential, research must focus on methodologically sound studies using advanced algorithms and comprehensive datasets.
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