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Applications of artificial intelligence and machine learning in respiratory medicine
  1. Sherif Gonem1,2,
  2. Wim Janssens3,4,
  3. Nilakash Das3,
  4. Marko Topalovic3,5
  1. 1 Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK
  2. 2 Division of Respiratory Medicine, University of Nottingham, Nottingham, UK
  3. 3 Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
  4. 4 Department of Respiratory Diseases, University Hospitals Leuven, Leuven, Belgium
  5. 5 ArtiQ NV, Leuven, Belgium
  1. Correspondence to Dr Sherif Gonem, Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham NG5 1PB, UK; sherif.gonem{at}


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.

  • imaging/CT MRI etc
  • histology/cytology
  • lung physiology

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  • Contributors SG conceived the idea for the manuscript, undertook literature search, co-wrote the first draft, and prepared the final draft and figures; WJ, ND and MT undertook literature search, co-wrote the first draft and critically appraised the final draft.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests SG has received speaker’s fees from Teva and consultancy fees from Anaxsys and 3M. WJ has received grants from AstraZeneca, Chiesi and GSK. WJ and MT are co-founders of ArtiQ, a spinoff company of KU Leuven. ND has no competing interests to declare.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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