Artificial intelligence and machine learning in respiratory medicine

ABSTRACT

Introduction

The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.

Areas covered

We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string ‘(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)’ was used in PubMed as a search strategy. The majority of studies identified corresponded to the area of chronic obstructive pulmonary disease (COPD), in particular to COPD and chest computed tomography scans, interpretation of pulmonary function tests, exacerbations and treatment. Another field of interest is the application of AI and ML to the diagnosis of interstitial lung disease, and a few other studies were identified on the fields of mechanical ventilation, interpretation of images on chest X-ray and diagnosis of bronchial asthma.

Expert opinion

ML may help to make clinical decisions but will not replace the physician completely. Human errors in medicine are associated with large financial losses, and many of them could be prevented with the help of AI and ML. AI is particularly useful in the absence of conclusive evidence of decision-making.

KEYWORDS:

• Artificial intelligence
• machine learning
• respiratory diseases
• COPD
• pulmonary fibrosis

Article highlights

• Artificial intelligence could generate models incorporating huge amounts of data that are incomprehensible to the physician

• Machine learning is part of artificial intelligence, where computers use statistical methods for self-learning without being explicitly programmed

• In many cases, machine learning would help to make clinical decisions by a doctor, but would not replace him/her completely

• In the field of respiratory medicine, several studies focused on obstructive conditions and pulmonary fibrosis with regards to diagnosis, staging, exacerbations, and survival.

• AI is particularly useful in the absence of conclusive evidence for decision-making

• The availability of very large datasets and the increasing capability of machine learning approaches may increase the clinical benefit and minimize the patient risk

Declaration of interest

M Miravitlles has received speaker or consulting fees from AstraZeneca, Bial, Boehringer Ingelheim, Chiesi, Cipla, CSL Behring, Laboratorios Esteve, Ferrer, Gebro Pharma, GlaxoSmithKline, Grifols, Menarini, Mereo Biopharma, Novartis, pH Pharma, Rovi, TEVA, Verona Pharma and Zambon, and research grants from GlaxoSmithKline and Grifols. unrelated to this manuscript. E Mekov has received speaker or consulting fees from AstraZeneca and Chiesi, unrelated to this manuscript. R Petkov has received speaker or consulting fees from AstraZeneca, Boehringer Ingelheim and Chiesi, unrelated to this manuscript. 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

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.