OBSTRUCTIVE, OCCUPATIONAL AND ENVIRONMENTAL DISEASES: Edited by Manish Joshi and Basil VarkeyArtificial intelligence in diagnosis of obstructive lung disease current status and future potentialDas, Nilakash; Topalovic, Marko; Janssens, WimAuthor Information Laboratory of Respiratory Diseases, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Belgium Correspondence to Wim Janssens, Laboratory of Respiratory Diseases, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, O&N1 Herestraat 49 bus 706, 3000 Leuven, Belgium. Tel: +32 16340957; e-mail: [email protected] Current Opinion in Pulmonary Medicine: March 2018 - Volume 24 - Issue 2 - p 117-123 doi: 10.1097/MCP.0000000000000459 Buy Metrics Abstract Purpose of review The application of artificial intelligence in the diagnosis of obstructive lung diseases is an exciting phenomenon. Artificial intelligence algorithms work by finding patterns in data obtained from diagnostic tests, which can be used to predict clinical outcomes or to detect obstructive phenotypes. The purpose of this review is to describe the latest trends and to discuss the future potential of artificial intelligence in the diagnosis of obstructive lung diseases. Recent findings Machine learning has been successfully used in automated interpretation of pulmonary function tests for differential diagnosis of obstructive lung diseases. Deep learning models such as convolutional neural network are state-of-the art for obstructive pattern recognition in computed tomography. Machine learning has also been applied in other diagnostic approaches such as forced oscillation test, breath analysis, lung sound analysis and telemedicine with promising results in small-scale studies. Summary Overall, the application of artificial intelligence has produced encouraging results in the diagnosis of obstructive lung diseases. However, large-scale studies are still required to validate current findings and to boost its adoption by the medical community. Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.