Artificial intelligence in pediatric allergy research
Lisik, Daniil ; Basna, Rani LU
; Dinh, Tai
; Hennig, Christian
; Shah, Syed Ahmar
; Wennergren, Göran
; Goksör, Emma
and Nwaru, Bright I.
(2025)
In European Journal of Pediatrics
184(1).
- 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... (More)
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. (Table presented.)
(Less)
- author
- Lisik, Daniil
; Basna, Rani
LU
; Dinh, Tai
; Hennig, Christian
; Shah, Syed Ahmar
; Wennergren, Göran
; Goksör, Emma
and Nwaru, Bright I.
- organization
- publishing date
- 2025-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Allergic rhinitis, Allergy, Artificial intelligence, Asthma, Atopic dermatitis, Childhood, Children, Eczema, Infants, Machine learning, Pediatrics, Teenagers, Wheezing
- in
- European Journal of Pediatrics
- volume
- 184
- issue
- 1
- article number
- 98
- publisher
- Springer
- external identifiers
-
- pmid:39706990
- scopus:85212779451
- ISSN
- 0340-6199
- DOI
- 10.1007/s00431-024-05925-5
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2024.
- id
- 5fd804d0-06f5-43c7-9a5a-c7ba02dded07
- date added to LUP
- 2025-01-08 15:20:14
- date last changed
- 2025-05-29 09:54:36
@article{5fd804d0-06f5-43c7-9a5a-c7ba02dded07,
abstract = {{<p>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. (Table presented.)</p>}},
author = {{Lisik, Daniil and Basna, Rani and Dinh, Tai and Hennig, Christian and Shah, Syed Ahmar and Wennergren, Göran and Goksör, Emma and Nwaru, Bright I.}},
issn = {{0340-6199}},
keywords = {{Allergic rhinitis; Allergy; Artificial intelligence; Asthma; Atopic dermatitis; Childhood; Children; Eczema; Infants; Machine learning; Pediatrics; Teenagers; Wheezing}},
language = {{eng}},
number = {{1}},
publisher = {{Springer}},
series = {{European Journal of Pediatrics}},
title = {{Artificial intelligence in pediatric allergy research}},
url = {{http://dx.doi.org/10.1007/s00431-024-05925-5}},
doi = {{10.1007/s00431-024-05925-5}},
volume = {{184}},
year = {{2025}},
}