Artificial intelligence responses to bronchiectasis patients’ questions: the AIR-BE study
Year: 2026
Type: article
Abstract: Background: The popularity of Generative Artificial Intelligence (AI)-powered chatbots is growing, with more patients using AI to understand respiratory conditions, including bronchiectasis. The quality of AI’s responses to bronchiectasis-related questions has not previously been evaluated. Objectives: To evaluate the reliability, accuracy, comprehensiveness, and understandability of responses generated by different AI-based chatbots to bronchiectasis-related questions formulated by patients. Design: Cross-sectional international study. Methods: People living with bronchiectasis from the EMBARC/European Lung Foundation (ELF) patient advisory group formulated 15 bronchiectasis-related questions, categorised into 3 difficulty tiers. These questions were submitted to three AI-based chatbots: ChatGPT, Bard, and Copilot. An international group of 28 experts from the European Respiratory Societies (recruited through Assemblies and the CONNECT clinical research collaboration) and 33 patients evaluated the answers’ reliability, accuracy, comprehensiveness, and understandability. Results: Of 45 outcomes, 37 were deemed reliable. Median accuracy scores ranged from 6.0 to 9.0, with ChatGPT scoring higher than Copilot and Bard (8.0 [7.0–9.0] vs 7.0 [6.0–8.0] vs 7.0 [6.0–8.0], p -value < 0.001). Median comprehensiveness scores ranged from 6.0 to 9.0, with ChatGPT providing more comprehensive answers compared to Bard and Copilot (8.0 [7.0–9.0] vs 8.0 [6.0–9.0] vs 7.0 [6.0–8.0], p -value < 0.001). Median understandability scores ranged from 7.0 to 10.0, with ChatGPT and Bard delivering more understandable answers than Copilot (9.0 [8.0–10.0] vs 9.0 [8.0–10.0] vs 9.0 [7.0–10.0], p -value < 0.001). Differences in understandability were noted in Bard’s responses across difficulty tiers (easy 9.0 [8.0–10.0] vs medium 9.0 [7.0–10.0] vs difficult 8.0 [7.0–10.0], p -value = 0.012). Conclusion: AI software, particularly ChatGPT, provided accurate, comprehensive, and understandable answers to our 15 bronchiectasis-related questions.
Authors: Mattia Nigro, Andrea Aliverti, Alessandra Angelucci, Apostolos Bossios, Hilary Pinnock, Jeanette Boyd, Pippa Powell, Michal Shteinberg, James D. Chalmers, Stefano Aliberti, Josje Altenburg, Alan F. Barker, Salvatore Battaglia, Francesco Blasi, Hayoung Choi, Valentina Conio, Chiara Contin, Megan L. Crichton, David de la Rosa-Carillo, Federico L. Dente, Charles Feldman, Patrick Flume, Pieter C. Goeminne, Andrea Gramegna, Alessandra Iacovelli, Shailesh B. Kolekar, Irena F. Laska, Michael R. Loebinger, Rosario Menendez, Alfredo Pachas, Felix C. Ringshausen, Giulia Scioscia, Umberto Semenzato, Angela Tramontano, Oriol Sibila, Eva Van Braeckel, Kevin L. Winthrop
Institutions: Humanitas University, IRCCS Humanitas Research Hospital, Politecnico di Milano, Karolinska University Hospital, Karolinska Institutet, University of Edinburgh, European Lung Foundation, Technion – Israel Institute of Technology, Carmel Medical Center, University of Dundee, Amsterdam University Medical Centers, University of Amsterdam, Oregon Health & Science University, University of Palermo, University of Milan, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Hallym University Kangnam Sacred Heart Hospital, University of Pavia, Policlinico San Matteo Fondazione, Ospedale dell' Angelo, Ninewells Hospital, Hospital de Sant Pau, University of Pisa, University of the Witwatersrand, Medical University of South Carolina, Policlinico Umberto I, Sapienza University of Rome, University of Copenhagen, Roskilde Sygehus, Zealand University Hospital, Footscray Hospital, Western Health, Royal Brompton Hospital, Imperial College London, Hospital Universitari i Politècnic La Fe, Instituto de Investigación Sanitaria La Fe, Medizinische Hochschule Hannover, German Center for Lung Research, University of Foggia, University of Padua, Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Hospital Clínic de Barcelona, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Universitat de Barcelona, Ghent University Hospital, Ghent University, Portland State University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W7213554349