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Applications of artificial intelligence and machine learning in respiratory medicine
- Source :
- Thorax. 75:695-701
- Publication Year :
- 2020
- Publisher :
- BMJ, 2020.
-
Abstract
- 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.
- Subjects :
- Pulmonary and Respiratory Medicine
0303 health sciences
Thoracic imaging
Process (engineering)
business.industry
Lung pathology
Machine learning
computer.software_genre
Histology/cytology
Machine Learning
Respiratory Medicine
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
030220 oncology & carcinogenesis
Pattern recognition (psychology)
Pulmonary Medicine
Humans
Deep neural networks
Medicine
Artificial intelligence
Applications of artificial intelligence
business
computer
030304 developmental biology
Subjects
Details
- ISSN :
- 14683296 and 00406376
- Volume :
- 75
- Database :
- OpenAIRE
- Journal :
- Thorax
- Accession number :
- edsair.doi.dedup.....ecc0f569c3b3e24c66a64a8e084a668b