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

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Abstract

Background

The 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.

Objective

In 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.

Methods

Using 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.

Results

Younger 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.

Conclusions

Older 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.

Related collections

Most cited references 38

Record: found
Abstract: not found
Article: not found

Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

Fred D. Davis (1989)

0 comments Cited 5186 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto Chacón-Novoa … (2017)

Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

0 comments Cited 2408 times – based on 0 reviews      Review now

Record: found
Abstract: not found
Article: not found

The inevitable application of big data to health care.

Travis Murdoch, Allan Detsky (2013)

0 comments Cited 460 times – based on 0 reviews      Review now

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Author and article information

Contributors

Karin Cinalioglu:

ORCID: https://orcid.org/0000-0003-2063-0790

Department of PsychiatryLady Davis Institute for Medical ResearchJewish General Hospital3755 Chem de la Côte-Sainte-CatherineMontreal, QC, H3T 1E2Canada1 5145508683karin.cinalioglu@mail.mcgill.ca

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: 2023

Publication date (Electronic): 28 April 2023

Volume: 25

Electronic Location Identifier: e38169

Affiliations

[1 ] Department of Psychiatry Lady Davis Institute for Medical Research Jewish General Hospital Montreal, QC Canada

[2 ] Department of Psychiatry Faculty of Medicine and Health Sciences McGill University Montreal, QC Canada

[3 ] Department of Psychology Université du Québec à Montréal (UQAM) Montreal, QC Canada

[4 ] Temerty Faculty of Medicine University of Toronto Toronto, ON Canada

[5 ] Department of Psychiatry McLean Hospital Harvard Medical School Boston, MA United States

Author notes

Corresponding Author: Karin Cinalioglu karin.cinalioglu@ 123456mail.mcgill.ca

Author information

Karin Cinalioglu https://orcid.org/0000-0003-2063-0790

Sasha Elbaz https://orcid.org/0000-0001-5716-1049

Kerman Sekhon https://orcid.org/0000-0001-6691-9931

Chien-Lin Su https://orcid.org/0000-0002-0664-8583

Soham Rej https://orcid.org/0000-0002-3908-9124

Harmehr Sekhon https://orcid.org/0000-0002-1967-6858

Article

Publisher ID: v25i1e38169

DOI: 10.2196/38169

PMC ID: 10182456

PubMed ID: 37115588

SO-VID: 90c32d55-2482-403d-976c-4e48a34c442e

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 28 June 2022

Date revision requested : 20 September 2022

Date revision received : 14 November 2022

Date accepted : 19 December 2022

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