Authors
Oluwatobi Olayiwola, Dinah Foer
Published in
Current allergy and asthma reports. Volume 26. Issue 1. Jun 29, 2026. Epub Jun 29, 2026.
Abstract
The review examines the application of machine learning (ML) and large language models (LLMs) to asthma management. We sought to identify clinically relevant applications, and particularly those that harness electronic health record data. We review methodological challenges and future directions for translating these tools into meaningful improvements in asthma care.
ML applied to electronic health record data has been utilized across several domains of asthma management: predicting medication response to inhaled corticosteroids and biologics, improving inhaler adherence through digital inhaler systems, and predicting exacerbation risk with moderate-to-high accuracy. Tools for patient education include clinician-guided chatbots, as well as publicly available LLMs. Limitations include accuracy, hallucinations, and patient health literacy. ML and LLMs offer promising pathways towards personalized, data-driven asthma management. However, harnessing this potential will require rigorous external validation, transparent model design, equitable implementation, and adaptive clinician oversight. Collaboration among clinicians, data scientists, and policymakers will be essential to implement these tools for patient-centered asthma care.
PMID:
42371357
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.
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