Source: Eur Respir J, 53 (4) 1801660; 10.1183/13993003.01660-2018



Rating:

Share or cite this content

Citations should be made in the following way:
Marko Topalovic, Nilakash Das, Pierre-Régis Burgel, Marc Daenen, Eric Derom, Christel Haenebalcke, Rob Janssen, Huib A.M. Kerstjens, Giuseppe Liistro, Renaud Louis, Vincent Ninane, Christophe Pison, Marc Schlesser, Piet Vercauter, Claus F. Vogelmeier, Emiel Wouters, Jokke Wynants, Wim Janssens. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J, 53 (4) 1801660; 10.1183/13993003.01660-2018

Member's Comments

No comment yet.

Related content which might interest you: Artificial intelligence for pulmonary function test interpretation Source: Eur Respir J, 53 (6) 1900782; 10.1183/13993003.00782-2019 Year: 2019 Artificial intelligence for pulmonary function test interpretation Source: Eur Respir J, 53 (6) 1900638; 10.1183/13993003.00638-2019 Year: 2019 Pulmonologists collaborate with explainable artificial intelligence for superior interpretation of pulmonary function tests Source: Virtual Congress 2021 – Digital health interventions in respiratory medicine Year: 2021 Artificial intelligence improves experts in reading pulmonary function tests Source: International Congress 2018 – The importance of the pulmonary function test in different clinical settings Year: 2018 Late Breaking Abstract - Applying artificial intelligence on pulmonary function tests improves the diagnostic accuracy Source: International Congress 2017 – Best abstracts in lung function Year: 2017 Artificial intelligence in the clinic: possible roles for pulmonary rehabilitation tests Source: Virtual Congress 2021 – Digital health in respiratory medicine: opportunities for everyone Year: 2021 Explaining predictions of an automated pulmonary function test interpretation algorithm Source: International Congress 2019 – M-health/e-health I Year: 2019 No need for pulmonologists to interpret pulmonary function tests Source: Eur Respir J, 54 (1) 1900829; 10.1183/13993003.00829-2019 Year: 2019 LATE-BREAKING ABSTRACT: Artificial intelligence to improve the diagnostic power of complete pulmonary function tests Source: International Congress 2016 – The best is yet to come in terms of lung function Year: 2016 Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19 Source: Eur Respir Rev, 29 (157) 200181; 10.1183/16000617.0181-2020 Year: 2020 Introduction of a new approach to interpret pulmonary function tests (PFT) based on Machine learning and Game theory Source: International Congress 2019 – Assessment and management of immune-mediated interstitial lung diseases Year: 2019 Can recursive partitioning empirically derive a pulmonary function test interpretive algorithm Source: International Congress 2015 – New developments in medical education, web and internet Year: 2015 Interpreting pulmonary function tests Source: Breathe 2009; 6: 102-110 Year: 2009 Artificial intelligence in pulmonary medicine Source: Virtual Congress 2021 – Scientific year in review Year: 2021 Biologic quality control variability among pulmonary function testing systems in a large academic outpatient pulmonary function laboratory Source: Annual Congress 2013 –Expiration, exhalation and exhaustion: measures of dynamic volumes, breath analysis and respiratory muscles Year: 2013 Interpretation of pulmonary function tests and preoperative assessment Source: International Congress 2014 – PG03 Respiratory medicine: diagnosis to treatment from a mentor and junior perspective Year: 2014 Pulmonary function tests in clinical practice Source: Annual Congress 2013 –PG17 Diagnostic tests for paediatric pulmonologists Year: 2013 Feasibility and reproducibility of pulmonary function tests in preschoolers Source: Annual Congress 2011 - Lung function today and tomorrow I Year: 2011 Paediatric lung function tests reference values: appropriateness and interpretation Source: International Congress 2015 – ME10 Paediatric lung function tests reference values Year: 2015