Evidence map›Paper›PMID 37115588›Full record

ArticleJournal of medical Internet research2023

Exploring Differential Perceptions of Artificial Intelligence in Health Care Among Younger Versus Older Canadians: Results From the 2021 Canadian Digital Health Survey.

Karin Cinalioglu, Sasha Elbaz, Kerman Sekhon, Chien-Lin Su, Soham Rej, Harmehr Sekhon

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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

  1. Pooled it
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  7. Does your generation influence your perception of the application of Artificial Intelligence in medicine?European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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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

6 authors.

Karin Cinalioglu *Department of Psychiatry, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.ORCID 0000-0003-2063-0790
Sasha Elbaz *Department of Psychology, Université du Québec à Montréal (UQAM), Montreal, QC, Canada.ORCID 0000-0001-5716-1049
Kerman SekhonTemerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-6691-9931
Chien-Lin SuDepartment of Psychiatry, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.ORCID 0000-0002-0664-8583
Soham RejDepartment of Psychiatry, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.ORCID 0000-0002-3908-9124
Harmehr SekhonDepartment of Psychiatry, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.ORCID 0000-0002-1967-6858

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe changing landscape of health care has led to the incorporation of powerful new technologies like artificial intelligence (AI) to assist with various services across a hospital. However, despite the potential outcomes that this tool may provide, little work has examined public opinion regarding their use.

objectiveIn this study, we aim to explore differences between younger versus older Canadians with regard to the level of comfort and perceptions around the adoption and use of AI in health care settings.

methodsUsing data from the 2021 Canadian Digital Health Survey (n=12,052), items related to perceptions about the use of AI as well as previous experience and satisfaction with health care were identified. We conducted Mann-Whitney U tests to compare the level of comfort of younger versus older Canadians regarding the use of AI in health care for a variety of purposes. Multinomial logistic regression was used to predict the comfort ratings based on categorical indicators.

resultsYounger Canadians had greater knowledge of AI, but older Canadians were more comfortable with AI applied to monitoring and predicting health conditions, decision support, diagnostic imaging, precision medicine, drug and vaccine development, disease monitoring at home, tracking epidemics, and optimizing workflow to save time. Additionally, for older respondents, higher satisfaction led to higher comfort ratings. Only 1 interaction effect was identified between previous experience, satisfaction, and comfort with AI for drug and vaccine development.

conclusionsOlder Canadians may be more open to various applications of AI within health care than younger Canadians. High satisfaction may be a critical criterion for comfort with AI, especially for older Canadians. Additionally, in the case of drug and vaccine development, previous experience may be an important moderating factor. We conclude that gaining a greater understanding of the perceptions of all health care users is integral to the implementation and sustainability of new and cutting-edge technologies in health care settings.

Indexed as

Artificial IntelligenceDelivery of Health CareAgedCanadaHealth SurveysHumansNorth American Peopleadoptionartificial intelligenceattitudeCanadaCanadiannational surveyolder adultperceptionpublic opinionsatisfactiontechnologytelehealthtelemedicinetrust

Identifiers

PMID37115588
PMCPMC10182456

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.