RT Journal Article
SR Electronic
T1 Applications of artificial intelligence and machine learning in respiratory medicine
JF Thorax
JO Thorax
FD BMJ Publishing Group Ltd and British Thoracic Society
SP 695
OP 701
DO 10.1136/thoraxjnl-2020-214556
VO 75
IS 8
A1 Sherif Gonem
A1 Wim Janssens
A1 Nilakash Das
A1 Marko Topalovic
YR 2020
UL http://thorax.bmj.com/content/75/8/695.abstract
AB The past 5 years have seen an explosion of interest in the use of artificial intelligence (AI) and machine learning techniques in medicine. This has been driven by the development of deep neural networks (DNNs)—complex networks residing in silico but loosely modelled on the human brain—that can process complex input data such as a chest radiograph image and output a classification such as ‘normal’ or ‘abnormal’. DNNs are ‘trained’ using large banks of images or other input data that have been assigned the correct labels. DNNs have shown the potential to equal or even surpass the accuracy of human experts in pattern recognition tasks such as interpreting medical images or biosignals. Within respiratory medicine, the main applications of AI and machine learning thus far have been the interpretation of thoracic imaging, lung pathology slides and physiological data such as pulmonary function tests. This article surveys progress in this area over the past 5 years, as well as highlighting the current limitations of AI and machine learning and the potential for future developments.