Evidence map›Paper›PMID 41946543›Full record

ArticleBMJ open2026

A double-blind, crossover, non-inferiority randomised controlled trial where primary care providers and patients compare human-generated and AI-generated digital health messages: the AI-CARE study protocol.

Audrée Lemieux, Stephen A Kutcher, Borris Rosnay Galani Tietcheu, Gretchen Seitz, Jason Trickovic, Douglas Archibald, Sylvie Grosjean, William Hogg, Sharon Johnston

Registry-linked trialAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06997107 (AI-CARE), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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.

NCT06997107 naactive not recruitingnot on this map

AI-CARE: AI to Create Accessible & Reliable Patient Education Materials

TypeinterventionalSponsorHopital MontfortRan2025 to 2026Enrolled50ConditionsPrimary Care PatientsArmsHealth promotional messages generated by Artificial Intelligence, Health promotional messages generated by humans
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. 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

9 authors.

Audrée LemieuxInstitut du Savoir Montfort, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-5150-8010
Stephen A KutcherCHEO Research Institute, Ottawa, ON, Canada.
Borris Rosnay Galani TietcheuInstitut du Savoir Montfort, Ottawa, ON, Canada.
Gretchen SeitzEastern Ontario Health Unit, Cornwall, ON, Canada.
Jason TrickovicDepartment of Family Medicine, University of Ottawa Faculty of Medicine, Ottawa, ON, Canada.
Douglas ArchibaldDepartment of Family Medicine, University of Ottawa Faculty of Medicine, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0001-6751-9788
Sylvie GrosjeanInstitut du Savoir Montfort, Ottawa, ON, Canada.ORCID http://orcid.org/0000-0002-4568-0500
William HoggInstitut du Savoir Montfort, Ottawa, ON, Canada.
Sharon JohnstonInstitut du Savoir Montfort, Ottawa, ON, Canada sharonjohnston@montfort.on.ca.ORCID http://orcid.org/0000-0001-8382-0378

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPrimary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been shown to reach a diverse patient population. With the uptake of Generative Artificial Intelligence (GenAI) usage in healthcare, there is an important opportunity to rapidly create messages that are tailored to different populations and conditions. However, thoroughly assessing artificial intelligence (AI)-generated content is essential, as GenAI raises concerns regarding its accuracy, understandability, actionability and bias perpetuation. We aim to investigate whether digital health messages created by GenAI are evaluated as non-inferior compared with those created by human experts. METHODS AND ANALYSIS: The AI-CARE (AI to Create Accessible and Reliable patient Education materials) study is a double-blind, crossover, non-inferiority randomised controlled trial. Data collection began on 30 May 2025, and is expected to be completed at the end of May 2026. Over 12 months, 192 messages on 48 topics will be written: half by primary care and public health experts and half by a GenAI tool (OpenAI's ChatGPT). Review Panels composed of 24 primary care providers and 24 patients will evaluate these messages using an Evaluation Grid developed to assess the messages' quality of information, adaptation to the target audience, relevance and usefulness, and readiness to be shared with patients. Evaluations will be completed via online REDCap (Research Electronic Data Capture) surveys and the order in which the 192 messages appear will be randomised and will vary between individuals. Participants and analysts will be blinded to the generation source. The primary outcome will be the Clarity and Understandability score. ETHICS AND DISSEMINATION: The Research Ethics Boards of the Hôpital Montfort (24-25-11-038) and the University of Ottawa (S-12-24-11153) formally approved this study in December 2024. Reported data will be grouped and anonymised for dissemination in peer-reviewed scientific journals and conferences. TRIAL REGISTRATION NUMBER: NCT06997107.

Indexed as

Artificial IntelligencePatient Education as TopicPrimary Health CareCross-Over StudiesDigital HealthDigital MediaDouble-Blind MethodEquivalence Trials as TopicGenerative Artificial IntelligenceHumansRandomized Controlled Trials as TopicArtificial IntelligenceDigital TechnologyHealth EducationPrimary Care

Identifiers

PMID41946543
PMCPMC13064138

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.