Evidence map›Paper›PMID 34898458›Full record

ArticleJMIR medical education2021

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.

Rebecca Charow, Tharshini Jeyakumar, Sarah Younus, Elham Dolatabadi, Mohammad Salhia, Dalia Al-Mouaswas, Melanie Anderson, Sarmini Balakumar, Megan Clare, Azra Dhalla and 11 more

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR medical education, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
105citing papers in PubMed, 6 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

105 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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45 more citing papers are in PubMed but not listed here.

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

21 authors.

Rebecca CharowInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-8553-4006
Tharshini JeyakumarUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-4482-3637
Sarah YounusUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0728-7628
Elham DolatabadiInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-2236-2611
Mohammad SalhiaMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-0841-1456
Dalia Al-MouaswasMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5620-0832
Melanie AndersonUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9786-1656
Sarmini BalakumarInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-6061-5929
Megan ClareMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2778-6002
Azra DhallaVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9806-3664
Caitlin GillanInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-1612-7423
Shabnam HaghzareUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-7973-2586
Ethan JacksonVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8988-302X
Nadim LalaniVector Institute, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-7570-4681
Jane MattsonMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8121-6126
Wanda PeteanuMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-0119-3364
Tim TrippUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-3967-2181
Jacqueline WaldorfMichener Institute of Education, University Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-1922-0575
Spencer WilliamsUniversity Health Network, Toronto, ON, Canada.ORCID https://orcid.org/0000-0003-3799-5462
Walter TavaresInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-8267-9448
David WiljerInstitute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-2748-2658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.

objectiveWith a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.

methodsAfter the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).

resultsOf the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.

conclusionsThis review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.

Indexed as

deep learningeducationhealth care providerslearningmachine learningpatient care

Identifiers

PMID34898458
PMCPMC8713099

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.