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Current state and prospects of artificial intelligence in allergy

van Breugel, Merlijn, Fehrmann, Rudolf S. N., Bügel, Marnix, Rezwan, Faisal I., Holloway, John W., Nawijn, Martijn C., Fontanella, Sara, Custovic, Adnan, Koppelman, Gerard H.·2023·Allergy
DOI 10.1111/all.15849
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Abstract

The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.

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FIGURE 1 Hierarchy of disciplines of artificial intelligence. The discipline of AI is often categorized as part of computer science, although it also builds upon other fields, such as mathematics and cognitive sciences. ML is a subdiscipline of AI, whereas deep learning is a further specialization within ML, generally characterized by large-­ scale artificial neural networks consisting of many layers, hence the term deep . The right part of the figure displays typical terms that one encounters within the (sub)discipline.
FIGURE 1 Hierarchy of disciplines of artificial intelligence. The discipline of AI is often categorized as part of computer science, although it also builds upon other fields, such as mathematics and cognitive sciences. ML is a subdiscipline of AI, whereas deep learning is a further specialization within ML, generally characterized by large-­ scale artificial neural networks consisting of many layers, hence the term deep . The right part of the figure displays typical terms that one encounters within the (sub)discipline.CC-BY-NC · From the PDF
FIGURE 3 Difference between ordinary least squares (OLS), machine learning (ML), and deep learning (DL) methodology. (A) In OLS, features (or predictors) are modeled manually, and their relationship is assumed linear to the output variable unless specified differently. Interpretation of the model and learned patterns (inference) is straightforward. (B) A similar procedure is followed with ML, but the algorithm can learn more complex patterns from the provided features. Nevertheless, thorough feature engineering by the practitioner is a critical step for delivering a performant model. (C) With DL, especially when applied to unstructured data, feature engineering is an inherent behavior of the interconnected neural network layers. The relationship between input features (tabular data fields, image pixels, text snippets, etc.) to the predicted output is more opaque and harder to interpret.
FIGURE 3 Difference between ordinary least squares (OLS), machine learning (ML), and deep learning (DL) methodology. (A) In OLS, features (or predictors) are modeled manually, and their relationship is assumed linear to the output variable unless specified differently. Interpretation of the model and learned patterns (inference) is straightforward. (B) A similar procedure is followed with ML, but the algorithm can learn more complex patterns from the provided features. Nevertheless, thorough feature engineering by the practitioner is a critical step for delivering a performant model. (C) With DL, especially when applied to unstructured data, feature engineering is an inherent behavior of the interconnected neural network layers. The relationship between input features (tabular data fields, image pixels, text snippets, etc.) to the predicted output is more opaque and harder to interpret.CC-BY-NC · From the PDF
Image 3
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Image 6
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FIGURE 6 Graphical summary of narrative review. This article provides a fundamental understanding of core AI concepts, its critical challenges, and the current and prospect opportunities of its application within the field of allergy.
FIGURE 6 Graphical summary of narrative review. This article provides a fundamental understanding of core AI concepts, its critical challenges, and the current and prospect opportunities of its application within the field of allergy.CC-BY-NC · From the PDF
Image 8
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Metadata

Authors
van Breugel, M., Fehrmann, R. S. N., Bügel, M., Rezwan, F. I., Holloway, J. W., Nawijn, M. C., Fontanella, S., Custovic, A., & Koppelman, G. H.
Affiliations
  • Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital University Medical Center Groningen, University of Groningen Groningen the Netherlands
  • Department of Medical Oncology University Medical Center Groningen, University of Groningen Groningen the Netherlands
  • MIcompany Amsterdam the Netherlands
  • Human Development and Health, Faculty of Medicine University of Southampton Southampton UK
  • Groningen Research Institute for Asthma and COPD (GRIAC) University Medical Center Groningen, University of Groningen Groningen the Netherlands
  • National Heart and Lung Institute Imperial College London London UK
ORCID
Venue
Allergy
Publisher
Wiley
Published
2023-08-16
Volume
78
Issue
10
Pages
2623-2643
ISSN
  • 0105-4538
  • 1398-9995
DOI
10.1111/all.15849
Cited by
43
References
34
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)

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