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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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Detalles Bibliográficos
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
Descripción
Sumario: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.