Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards

Background/Objectives: The aim of this study was to analyze whether the implementation of artificial intelligence (AI), specifically the Natural Language Processing (NLP) branch developed by OpenAI, could help a thoracic multidisciplinary tumor board (MTB) make decisions if provided with all of the...

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Detalhes bibliográficos
Autores: Zabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91b, Aguinagalde, B. (Borja)|||/items/610953dd-f873-4f89-b1e3-9e952fd4e058, López, I. (Iker)|||/items/5ba4b5c0-5eb6-4785-a623-57c1dacec1e8, Fernández-Monge, A. (Arantza)|||/items/12a8c2c4-8cd5-44f4-8ae4-3c38f7bf9cc5, Lizarbe, J.A. (Jon Ander)|||/items/e7287387-f79f-4a5c-87fc-d01016352cd6, Mainer, M. (María)|||/items/e59f525f-6c38-4929-b670-aecf64179791, Ferrer-Bonsoms, J.A. (Juan Ángel)|||/items/cfd30c63-00b2-4b7d-8deb-f34472a86ee5, Assas, M. (Mateo) de|||/items/98b4e037-e646-44b0-80e0-50def23a00a8
Tipo de documento: artigo
Data de publicação:2025
País:España
Recursos:Universidad de Navarra
Repositório:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglês
OAI Identifier:oai:dadun.unav.edu:10171/119441
Acesso em linha:https://hdl.handle.net/10171/119441
Access Level:Acceso aberto
Palavra-chave:Artificial intelligence (AI)
Lung cancer
Multidisciplinary tumor board
Decision making
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spelling Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor BoardsZabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91bAguinagalde, B. (Borja)|||/items/610953dd-f873-4f89-b1e3-9e952fd4e058López, I. (Iker)|||/items/5ba4b5c0-5eb6-4785-a623-57c1dacec1e8Fernández-Monge, A. (Arantza)|||/items/12a8c2c4-8cd5-44f4-8ae4-3c38f7bf9cc5Lizarbe, J.A. (Jon Ander)|||/items/e7287387-f79f-4a5c-87fc-d01016352cd6Mainer, M. (María)|||/items/e59f525f-6c38-4929-b670-aecf64179791Ferrer-Bonsoms, J.A. (Juan Ángel)|||/items/cfd30c63-00b2-4b7d-8deb-f34472a86ee5Assas, M. (Mateo) de|||/items/98b4e037-e646-44b0-80e0-50def23a00a8Artificial intelligence (AI)Lung cancerMultidisciplinary tumor boardDecision makingBackground/Objectives: The aim of this study was to analyze whether the implementation of artificial intelligence (AI), specifically the Natural Language Processing (NLP) branch developed by OpenAI, could help a thoracic multidisciplinary tumor board (MTB) make decisions if provided with all of the patient data presented to the committee and supported by accepted clinical practice guidelines. Methods: This is a retrospective comparative study. The inclusion criteria were defined as all patients who presented at the thoracic MTB with a suspicious or first diagnosis of non-small-cell lung cancer between January 2023 and June 2023. Intervention: GPT 3.5 turbo chat was used, providing the clinical case summary presented in committee proceedings and the latest SEPAR lung cancer treatment guidelines. The application was asked to issue one of the following recommendations: follow-up, surgery, chemotherapy, radiotherapy, or chemoradiotherapy. Statistical analysis: A concordance analysis was performed by measuring the Kappa coefficient to evaluate the consistency between the results of the AI and the committee’s decision. Results: Fifty-two patients were included in the study. The AI had an overall concordance of 76%, with a Kappa index of 0.59 and a consistency and replicability of 92.3% for the patients in whom it recommended surgery (after repeating the cases four times). Conclusions: AI is an interesting tool which could help in decision making in MTBs.MDPIDadun. Depósito Académico Digital Universidad de Navarra20252025-01-0120252025-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/119441reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/1194412026-06-21T12:47:57Z
dc.title.none.fl_str_mv Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
title Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
spellingShingle Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
Zabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91b
Artificial intelligence (AI)
Lung cancer
Multidisciplinary tumor board
Decision making
title_short Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
title_full Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
title_fullStr Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
title_full_unstemmed Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
title_sort Utility of Artificial Intelligence for Decision Making in Thoracic Multidisciplinary Tumor Boards
dc.creator.none.fl_str_mv Zabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91b
Aguinagalde, B. (Borja)|||/items/610953dd-f873-4f89-b1e3-9e952fd4e058
López, I. (Iker)|||/items/5ba4b5c0-5eb6-4785-a623-57c1dacec1e8
Fernández-Monge, A. (Arantza)|||/items/12a8c2c4-8cd5-44f4-8ae4-3c38f7bf9cc5
Lizarbe, J.A. (Jon Ander)|||/items/e7287387-f79f-4a5c-87fc-d01016352cd6
Mainer, M. (María)|||/items/e59f525f-6c38-4929-b670-aecf64179791
Ferrer-Bonsoms, J.A. (Juan Ángel)|||/items/cfd30c63-00b2-4b7d-8deb-f34472a86ee5
Assas, M. (Mateo) de|||/items/98b4e037-e646-44b0-80e0-50def23a00a8
author Zabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91b
author_facet Zabaleta, J. (Jon)|||/items/85d49e17-a653-4897-a0c2-969289f5d91b
Aguinagalde, B. (Borja)|||/items/610953dd-f873-4f89-b1e3-9e952fd4e058
López, I. (Iker)|||/items/5ba4b5c0-5eb6-4785-a623-57c1dacec1e8
Fernández-Monge, A. (Arantza)|||/items/12a8c2c4-8cd5-44f4-8ae4-3c38f7bf9cc5
Lizarbe, J.A. (Jon Ander)|||/items/e7287387-f79f-4a5c-87fc-d01016352cd6
Mainer, M. (María)|||/items/e59f525f-6c38-4929-b670-aecf64179791
Ferrer-Bonsoms, J.A. (Juan Ángel)|||/items/cfd30c63-00b2-4b7d-8deb-f34472a86ee5
Assas, M. (Mateo) de|||/items/98b4e037-e646-44b0-80e0-50def23a00a8
author_role author
author2 Aguinagalde, B. (Borja)|||/items/610953dd-f873-4f89-b1e3-9e952fd4e058
López, I. (Iker)|||/items/5ba4b5c0-5eb6-4785-a623-57c1dacec1e8
Fernández-Monge, A. (Arantza)|||/items/12a8c2c4-8cd5-44f4-8ae4-3c38f7bf9cc5
Lizarbe, J.A. (Jon Ander)|||/items/e7287387-f79f-4a5c-87fc-d01016352cd6
Mainer, M. (María)|||/items/e59f525f-6c38-4929-b670-aecf64179791
Ferrer-Bonsoms, J.A. (Juan Ángel)|||/items/cfd30c63-00b2-4b7d-8deb-f34472a86ee5
Assas, M. (Mateo) de|||/items/98b4e037-e646-44b0-80e0-50def23a00a8
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Artificial intelligence (AI)
Lung cancer
Multidisciplinary tumor board
Decision making
topic Artificial intelligence (AI)
Lung cancer
Multidisciplinary tumor board
Decision making
description Background/Objectives: The aim of this study was to analyze whether the implementation of artificial intelligence (AI), specifically the Natural Language Processing (NLP) branch developed by OpenAI, could help a thoracic multidisciplinary tumor board (MTB) make decisions if provided with all of the patient data presented to the committee and supported by accepted clinical practice guidelines. Methods: This is a retrospective comparative study. The inclusion criteria were defined as all patients who presented at the thoracic MTB with a suspicious or first diagnosis of non-small-cell lung cancer between January 2023 and June 2023. Intervention: GPT 3.5 turbo chat was used, providing the clinical case summary presented in committee proceedings and the latest SEPAR lung cancer treatment guidelines. The application was asked to issue one of the following recommendations: follow-up, surgery, chemotherapy, radiotherapy, or chemoradiotherapy. Statistical analysis: A concordance analysis was performed by measuring the Kappa coefficient to evaluate the consistency between the results of the AI and the committee’s decision. Results: Fifty-two patients were included in the study. The AI had an overall concordance of 76%, with a Kappa index of 0.59 and a consistency and replicability of 92.3% for the patients in whom it recommended surgery (after repeating the cases four times). Conclusions: AI is an interesting tool which could help in decision making in MTBs.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-01-01
2025
2025-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/119441
url https://hdl.handle.net/10171/119441
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
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