Evidence map›Paper›PMID 41402777›Full record

ArticleRespiratory research2025

Accuracy, comprehensiveness and understandability of AI-generated answers to questions from people with COPD: the AIR-COPD Study.

Mattia Nigro, Greta E Behring, Andrea Aliverti, Alessandra Angelucci, Anita Kay Simonds, Antonio Anzueto, Peter Martin Calverley, Francesco Amati, Anna Stainer, Apostolos Bossios and 5 more

Abstract readMulticenter Study
In one paragraph

Article in Respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Mattia NigroDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
Greta E BehringDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
Andrea AlivertiDipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Piazza Leonardo Da Vinci 32, 20133, Milan, Italy.
Alessandra AngelucciDipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Piazza Leonardo Da Vinci 32, 20133, Milan, Italy.
Anita Kay SimondsSleep and Ventilation Unit, Royal Brompton and Harefield Hospital (Guys and St Thomas' NHS Foundation Trust), London, UK.
Antonio AnzuetoSouth Texas Veterans Health Care System, University of Texas, San Antonio, TX, USA.
Peter Martin CalverleyInstitute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK.
Francesco AmatiDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
Anna StainerDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy.
Apostolos BossiosKarolinska Severe Asthma Centre, Department of Respiratory Medicine and Allergy, Karolinska University Hospital, Stockholm, Sweden.
Hilary PinnockUsher Institute, The University of Edinburgh, Edinburgh, UK.
Jeanette BoydEuropean Lung Foundation, Sheffield, UK.
Pippa PowellEuropean Lung Foundation, Sheffield, UK.
Stefano AlibertiDepartment of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy. stefano.aliberti@hunimed.eu.ORCID http://orcid.org/0000-0002-0090-4531
AIR-COPD Task Force

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic obstructive pulmonary disease (COPD) remains an underestimated and underdiagnosed condition due to low disease awareness. Generative Artificial Intelligence (AI) chatbots are convenient and accessible sources of medical information, but evaluation of the quality of answers provided by patient-generated questions about COPD has not been performed to date.

objectiveTo assess and compare accuracy, comprehensiveness, understandability and reliability of different AI chatbots in response to patient-generated questions on the clinical management of COPD.

methodsA cross-sectional study was conducted in collaboration with the European Respiratory Society (ERS), the European Lung Foundation (ELF), and the ERS CONNECT Clinical Research Collaboration (CRC). Fifteen real questions formulated by ELF COPD patient representatives were divided into three difficulty tiers (easy, medium, difficult) and submitted to ChatGPT (version 3.5), Bard, and Copilot. Experts assessed accuracy and comprehensiveness on a 0–10 scale; patients assessed understandability using the same scale. Reliability was assessed by two investigators. Reviewers were blinded to which AI system generated the answers, and only those who completed all evaluations were included in the analysis.

resultsChatGPT responses were the most reliable (14/15), followed by Copilot (12/15) and Bard (11/15). ChatGPT scored higher for accuracy (8.0 [7.0 – 9.0]) and comprehensiveness (8.0 [6.8 – 9.0]) than Bard (6.0 [5.0 – 8.0] and 6.0 [5.0 – 7.0]) and Copilot (6.0 [5.0 – 7.3] and 6.0 [5.0 – 8.0]) (both P < 0.001). Understandability was similar across all software (ChatGPT: 8.0 [8.0–10.0]; Bard: 9.0 [8.0–10.0]; Copilot: 9.0 [8.0–10.0]) (P = 0.53). No significant effect was detected according to the difficulty of the question.

conclusionOur findings suggest that AI chatbots, particularly ChatGPT, can provide accurate, comprehensive and understandable answers to patients’ questions.

Indexed as

Artificial IntelligencePulmonary Disease, Chronic ObstructiveAgedCross-Sectional StudiesFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedReproducibility of ResultsSurveys and QuestionnairesAccuracyAIArtificial intelligenceComprehensivenessCOPDDisease awarenessReliabilityUnderstandability

Identifiers

PMID41402777
PMCPMC12821988

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.