Current state and prospects of artificial intelligence in allergy
Current state and prospects of artificial intelligence in allergy
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 and immunology. 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 sub typing. 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.
artificial intelligence, deep learning, diagnosis, machine learning, precision medicine
2623-2643
van Bruegel, Merlijn
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Fehrmann, Rudolf S.N.
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Bugel, Marnix
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Rezwan, Faisal I.
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Holloway, John W.
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Nawijn, Martijn C.
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Fontanella, Sara
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Custovic, A.
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Koppelman, Gerard H.
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1 October 2023
van Bruegel, Merlijn
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Fehrmann, Rudolf S.N.
011bf076-569a-40b5-8b2a-201c54865580
Bugel, Marnix
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Rezwan, Faisal I.
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Holloway, John W.
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Nawijn, Martijn C.
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Fontanella, Sara
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Custovic, A.
624645ad-f4d2-4b1f-a9b6-a8bd763a8d84
Koppelman, Gerard H.
db8a0204-1f42-4273-a8a7-8575a43cd21f
van Bruegel, Merlijn, Fehrmann, Rudolf S.N., Bugel, Marnix, Rezwan, Faisal I., Holloway, John W., Nawijn, Martijn C., Fontanella, Sara, Custovic, A. and Koppelman, Gerard H.
(2023)
Current state and prospects of artificial intelligence in allergy.
Allergy: European Journal of Allergy and Clinical Immunology, 78 (10), .
(doi:10.1111/all.15849).
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 and immunology. 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 sub typing. 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.
Text
van Breugel et al 2023 AI in Allergy [97]
- Accepted Manuscript
Restricted to Repository staff only until 31 July 2024.
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Accepted/In Press date: 31 July 2023
e-pub ahead of print date: 16 August 2023
Published date: 1 October 2023
Additional Information:
Funding Information:
The authors acknowledge the team members from the broader research collaboration between UMCG and MIcompany for their critical feedback on the positioning and narrative of this article.
Publisher Copyright:
© 2023 The Authors. Allergy published by European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
Keywords:
artificial intelligence, deep learning, diagnosis, machine learning, precision medicine
Identifiers
Local EPrints ID: 480601
URI: http://eprints.soton.ac.uk/id/eprint/480601
ISSN: 0105-4538
PURE UUID: 79ea8140-299a-406a-8d34-4255524eeb62
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Date deposited: 07 Aug 2023 16:44
Last modified: 18 Mar 2024 03:24
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Contributors
Author:
Merlijn van Bruegel
Author:
Rudolf S.N. Fehrmann
Author:
Marnix Bugel
Author:
Faisal I. Rezwan
Author:
Martijn C. Nawijn
Author:
Sara Fontanella
Author:
A. Custovic
Author:
Gerard H. Koppelman
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