Artificial Intelligence-Enhanced Spirometry in Primary Healthcare: Opportunities for Early Detection of Small Airway Dysfunction

Spirometry underuse and persistent limitations in its technical quality and interpretation in primary healthcare (PHC) contribute to delayed and inaccurate diagnosis of chronic obstructive pulmonary disease (COPD) and asthma. Artificial intelligence (AI) has recently demonstrated, at the randomised controlled trial level, that it can improve spirometry interpretation by primary care clinicians. Maximal mid-expiratory flow (MMEF), a spirometric measure routinely derived from every forced expiratory manoeuvre, has been associated with subsequent airflow obstruction and lung function decline, particularly when conventional spirometric indices remain preserved, suggesting that it may contribute to earlier recognition of small airway dysfunction. However, no existing review has examined whether AI could specifically address the challenge of MMEF interpretation, and the incremental value of MMEF within AI-assisted spirometry models remains unestablished. This narrative review synthesises current evidence on AI-assisted spirometry in PHC across three evidence domains, diagnostic classification, quality control, and computational small airway assessment, and examines the potential for integrating MMEF with conventional spirometric indices, flow–volume curve morphology, technical quality, clinical characteristics, and longitudinal data to support its clinical interpretation. The available evidence supports the feasibility of the individual components underpinning this proposed framework, including AI-assisted spirometry interpretation, automated quality assessment, and computational extraction of small airway information from spirometric data. However, the contextual integration of MMEF within AI-assisted models has not yet been prospectively evaluated, and validation of its incremental predictive value and clinical utility is needed before implementation can be recommended.