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...

Descripción completa

Detalles 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 recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/119441
Acceso en línea:https://hdl.handle.net/10171/119441
Access Level:acceso abierto
Palabra clave:Artificial intelligence (AI)
Lung cancer
Multidisciplinary tumor board
Decision making
Descripción
Sumario: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.

Similares en LA Referencia