Evidence map›Paper›PMID 39264934›Full record

ReviewPLOS digital health2024

Artificial intelligence and social accountability in the Canadian health care landscape: A rapid literature review.

Alex Anawati, Holly Fleming, Megan Mertz, Jillian Bertrand, Jennifer Dumond, Sophia Myles, Joseph Leblanc, Brian Ross, Daniel Lamoureux, Div Patel and 2 more

Abstract readReview
In one paragraph

Review in PLOS digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Alex AnawatiDr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada.ORCID https://orcid.org/0000-0001-5767-3781
Holly FlemingDr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada.
Megan MertzDr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada.
Jillian BertrandNOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada.
Jennifer DumondHealth Sciences Library, NOSM University, Sudbury/Thunder Bay, Ontario, Canada.
Sophia MylesSchool of Sociological and Anthropological Studies, University of Ottawa, Ottawa, Ontario, Canada.
Joseph LeblancDr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada.
Brian RossMedical Sciences Division, NOSM University, Sudbury/Thunder Bay, Ontario, Canada.
Daniel LamoureuxNOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada.ORCID https://orcid.org/0000-0001-5314-214X
Div PatelNOSM University, UME Learner, Sudbury/Thunder Bay, Ontario, Canada.ORCID https://orcid.org/0000-0002-0188-451X
Renald CarrierHealth Sciences North, Sudbury, Ontario, Canada.
Erin CameronDr. Gilles Arcand Centre for Health Equity, NOSM University, Thunder Bay/Sudbury, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSituated within a larger project entitled "Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence," this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada? METHODOLOGY: A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome. PRINCIPAL

findingsThe search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy.

conclusionThe findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.

Identifiers

PMID39264934
PMCPMC11392241

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.