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Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic int...

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Autores principales: Das, Nilakash, Happaerts, Sofie, Gyselinck, Iwein, Staes, Michael, Derom, Eric, Brusselle, Guy, Burgos, Felip, Contoli, Marco, Dinh-Xuan, Anh Tuan, Franssen, Frits M.E., Gonem, Sherif, Greening, Neil, Haenebalcke, Christel, Man, William D-C., Moisés, Jorge, Peché, Rudi, Poberezhets, Vitalii, Quint, Jennifer K., Steiner, Michael C., Vanderhelst, Eef, Abdo, Mustafa, Topalovic, Marko, Janssens, Wim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Respiratory Society 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196345/
https://www.ncbi.nlm.nih.gov/pubmed/37080566
http://dx.doi.org/10.1183/13993003.01720-2022
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author Das, Nilakash
Happaerts, Sofie
Gyselinck, Iwein
Staes, Michael
Derom, Eric
Brusselle, Guy
Burgos, Felip
Contoli, Marco
Dinh-Xuan, Anh Tuan
Franssen, Frits M.E.
Gonem, Sherif
Greening, Neil
Haenebalcke, Christel
Man, William D-C.
Moisés, Jorge
Peché, Rudi
Poberezhets, Vitalii
Quint, Jennifer K.
Steiner, Michael C.
Vanderhelst, Eef
Abdo, Mustafa
Topalovic, Marko
Janssens, Wim
author_facet Das, Nilakash
Happaerts, Sofie
Gyselinck, Iwein
Staes, Michael
Derom, Eric
Brusselle, Guy
Burgos, Felip
Contoli, Marco
Dinh-Xuan, Anh Tuan
Franssen, Frits M.E.
Gonem, Sherif
Greening, Neil
Haenebalcke, Christel
Man, William D-C.
Moisés, Jorge
Peché, Rudi
Poberezhets, Vitalii
Quint, Jennifer K.
Steiner, Michael C.
Vanderhelst, Eef
Abdo, Mustafa
Topalovic, Marko
Janssens, Wim
author_sort Das, Nilakash
collection PubMed
description BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. METHODS: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. RESULTS: In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. CONCLUSION: A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone.
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spelling pubmed-101963452023-05-20 Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation Das, Nilakash Happaerts, Sofie Gyselinck, Iwein Staes, Michael Derom, Eric Brusselle, Guy Burgos, Felip Contoli, Marco Dinh-Xuan, Anh Tuan Franssen, Frits M.E. Gonem, Sherif Greening, Neil Haenebalcke, Christel Man, William D-C. Moisés, Jorge Peché, Rudi Poberezhets, Vitalii Quint, Jennifer K. Steiner, Michael C. Vanderhelst, Eef Abdo, Mustafa Topalovic, Marko Janssens, Wim Eur Respir J Original Research Articles BACKGROUND: Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. METHODS: The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. RESULTS: In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. CONCLUSION: A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone. European Respiratory Society 2023-05-18 /pmc/articles/PMC10196345/ /pubmed/37080566 http://dx.doi.org/10.1183/13993003.01720-2022 Text en Copyright ©The authors 2023. https://creativecommons.org/licenses/by-nc/4.0/This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org (mailto:permissions@ersnet.org)
spellingShingle Original Research Articles
Das, Nilakash
Happaerts, Sofie
Gyselinck, Iwein
Staes, Michael
Derom, Eric
Brusselle, Guy
Burgos, Felip
Contoli, Marco
Dinh-Xuan, Anh Tuan
Franssen, Frits M.E.
Gonem, Sherif
Greening, Neil
Haenebalcke, Christel
Man, William D-C.
Moisés, Jorge
Peché, Rudi
Poberezhets, Vitalii
Quint, Jennifer K.
Steiner, Michael C.
Vanderhelst, Eef
Abdo, Mustafa
Topalovic, Marko
Janssens, Wim
Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title_full Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title_fullStr Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title_full_unstemmed Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title_short Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
title_sort collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation
topic Original Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10196345/
https://www.ncbi.nlm.nih.gov/pubmed/37080566
http://dx.doi.org/10.1183/13993003.01720-2022
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