Predictive models and applicability of artificial intelligence-based approaches in drug allergy

Núñez, Rafaela; Doña, Inmaculada; Cornejo-García, José Antonio. Current Opinion in Allergy and Clinical Immunology 24(4):p 189-194, August 2024. | DOI: 10.1097/ACI.0000000000001002

Abstract

Purpose of review 

Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings.

Recent findings 

Graphical summary of the development and
implementationof a ML model to diagnose drug allergy patients.

Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy.

Summary 

This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.

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