Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases

ABSTRACT

Introduction

Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

Areas covered

This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

Expert opinion

Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

KEYWORDS:

• Artificial intelligence
• asthma
• chronic obstructive pulmonary disease
• diagnosis
• machine learning
• management

Article highlights

• Artificial intelligence and machine learning can learn from big data, identify non-linear relationships, and uncover connections that healthcare professionals may overlook.

• Machine learning may improve the diagnosis and prognosis of asthma and chronic obstructive pulmonary disease by providing valuable support to physicians in clinical decision-making.

• A better understanding of machine learning by clinicians may overcome their resistance to the use of machine learning models and facilitate the widespread adoption of these techniques in healthcare.

• Future research should focus on conducting studies with larger samples and thoroughly validating machine learning models to ensure their generalizability and safety.

• Reinforcement learning and causal machine learning hold promise as future avenues for the management of chronic respiratory diseases.

Abbreviations

AI	=	Artificial Intelligence
AUC	=	Area under the Receiver Operating Characteristic Curve
BODE	=	Body mass index, airflow obstruction, dyspnea, exercise capacity
COPD	=	Chronic obstructive pulmonary disease
FEV1	=	Forced expiratory volume in one second
ML	=	Machine learning
ROC	=	Receiver Operating Characteristic

Declaration of interest

FME Franssen has obtained research grants from AstraZeneca, outside the scope of the current study. FME Franssen has obtained consultancy fees from MSD for advisory boards outside the scope of the current study. FME Franssen has received speakers’ fees by AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Chiesi and Novartis. MAS has obtained research grants from Netherlands Lung Foundation and Stichting Astma Bestrijding, outside the scope of the current study. MA Spruit has obtained research grants from AstraZeneca, TEVA, Chiesi and Boehringer Ingelheim for the current study. MA Spruit has obtained consultancy fees from AstraZeneca and Boehringer Ingelheim for advisory boards outside the scope of the current study. All research grants and consultancy fees were paid to Ciro. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

A reviewer on this manuscript received an honorarium from Expert Review of Respiratory Medicine for their review work. Peer reviewers on this manuscript have no other relevant financial relationships or otherwise to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17476348.2024.2302940

Additional information

Funding

Part of the work of J Antão and Q Deng is financially supported by AstraZeneca, Boehringer Ingelheim, Chiesi and TEVA.