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Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology
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“AI, on the other hand, excels at identifying hidden patterns, integrating data from diverse sources, and making predictions that go beyond conventional approaches [240].”
From Pollen–Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges (2024) · cites this paper as [240]
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“This predictive capability reduces trial and error in treatment, improving safety and efficiency [240].”
From Pollen–Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges (2024) · cites this paper as [240]
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“By analyzing baseline variables such as cytokine levels, allergen-specific IgE concentrations, and microbiome composition, these models can identify individuals who are more likely to achieve tolerance or experience adverse effects.”
From Pollen–Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges (2024) · cites this paper as [240]
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Metadata
- Authors
- MacMath, D., Chen, M., & Khoury, P.
- ORCID
- Khoury, Paneez
- Venue
- Current Allergy and Asthma Reports
- Published
- 2023-05-09
- Volume
- 23
- Issue
- 6
- Pages
- 351-362
- ISSN
- 1529-7322
- 1534-6315
- PMC
- PMC10169188
- Cited by
- 62
- References
- 17
- Language
- en
- Licence
- Funders
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases (1ZIEAI001148-11)
- National Institute of Allergy and Infectious Diseases (5R25AI147369-03)
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