TY - JOUR
T1 - Potential role for artificial intelligence/machine learning in the diagnosis of asthma and COPD
AU - Kaplan, Alan
AU - Cao, Hui
AU - FitzGerald, J. Mark
AU - Iannotti, Nick
AU - Kocks, Janwillem W H
AU - Kostikas, Konstantinos
AU - Price, David
AU - Reddel, Helen K.
AU - Tsiligianni, Ioanna
AU - Vogelmeier, Claus F
AU - Pfister, Pascal
AU - Mastoridis, Paul
N1 - Funding
Medical writing support for this manuscript was funded by Novartis Pharma AG. The content is solely the responsibility of the authors.
Acknowledgements
The authors thank Ian Wright, PhD, of Novartis Ireland Ltd, for providing medical writing support in accordance with Good Publication Practice (GPP3) guidelines (http://www.ismpp.org/gpp3).
PY - 2021/2/15
Y1 - 2021/2/15
N2 - Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly utilized in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is critical that clinicians review how AI can function in the context of heterogeneous conditions such as asthma and COPD where diagnostic criteria overlap. It will also be important to consider how AI use fits into everyday clinical practice and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future and it will be exciting to the see benefits that arise for patients and doctors from its use in everyday clinical practice.
AB - Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly utilized in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is critical that clinicians review how AI can function in the context of heterogeneous conditions such as asthma and COPD where diagnostic criteria overlap. It will also be important to consider how AI use fits into everyday clinical practice and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future and it will be exciting to the see benefits that arise for patients and doctors from its use in everyday clinical practice.
KW - artificial intelligence
KW - machine learning
KW - respiratory disease
KW - asthma
KW - COPD
KW - diagnosis
M3 - Article
JO - The Journal of Allergy and Clinical Immunology: In Practice
JF - The Journal of Allergy and Clinical Immunology: In Practice
SN - 2213-2198
ER -