Evidence map›Paper›PMID 37347537›Full record

ArticleJournal of medical Internet research2023

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Fernando Korn Malerbi, Luis Filipe Nakayama, Robyn Gayle Dychiao, Lucas Zago Ribeiro, Cleva Villanueva, Leo Anthony Celi, Caio Vinicius Regatieri

Open access · goldAbstract 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 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 10% of its field
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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Development of a Pilot Artificial Intelligence Education Curriculum for Hospital Staff.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Integrating Generative AI in Dental Education: A Scoping Review of Current Practices and Recommendations.European journal of dental education : official journal of the Association for Dental Education in Europe · 2025
    Article
  15. Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study.Health expectations : an international journal of public participation in health care and health policy · 2025
    Article
  16. Review
  17. Review
  18. Review
  19. Article
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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

7 authors at 4 institutions in 3 countries.

Fernando Korn MalerbiOphthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil.ORCID 0000-0002-6523-5172
Luis Filipe NakayamaOphthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil.ORCID 0000-0002-6847-6748
Robyn Gayle DychiaoUniversity of the Philippines College of Medicine, Manila, Philippines.ORCID 0000-0003-2371-9002
Lucas Zago RibeiroOphthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil.ORCID 0000-0001-9130-1500
Cleva VillanuevaEscuela Superior de Medicina, Instituto Politecnico Nacional, Mexico City, Mexico.ORCID 0000-0003-2694-5902
Leo Anthony CeliLaboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States.ORCID 0000-0001-6712-6626
Caio Vinicius RegatieriOphthalmology Department, Sao Paulo Federal University, Sao Paulo, Brazil.ORCID 0000-0003-1511-8696
Universidade Federal de São Paulo · BRHarvard University · USInstituto Politécnico Nacional · MXMassachusetts Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Indexed as

Artificial IntelligenceCurriculumEducational StatusHumansMachine LearningNeural Networks, Computerartificial intelligenceclinicaldatadatasetdigitaldigital educationdigital healtheducationevaluationhealth educationmachine learningnetworkneuralsettransformation

Identifiers

PMID37347537
PMCPMC10337407
OpenAlexW4381598523

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.