Evidence map›Paper›PMID 41552409›Full record

ArticleMedEdPublish (2016)2025

Applied insights for using Generative Artificial Intelligence in Faculty Development in Health Professions Education.

Melchor Sánchez-Mendiola, Megan Anakin, Ardi Findyartini, Rachel Levine, Ana Da Silva, Farhan Saeed Vakani

Abstract read
In one paragraph

Article in MedEdPublish (2016), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Melchor Sánchez-MendiolaPostgraduate Studies Division, National Autonomous University of Mexico Faculty of Medicine, Mexico City, Mexico City, 04510, Mexico.ORCID https://orcid.org/0000-0002-9664-3208
Megan AnakinPharmacy Education, The University of Sydney School of Pharmacy, Sydney, New South Wales, 2006, Australia.ORCID https://orcid.org/0000-0002-6499-7802
Ardi FindyartiniIndonesia Medical Education and Research Institute, University of Indonesia Faculty of Medicine, Jakarta, Special Capital Region of Jakarta, 16424, Indonesia.ORCID https://orcid.org/0000-0002-9601-3994
Rachel LevineFaculty Development Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA.
Ana Da SilvaNew Medical Education programmes, Swansea University Medical School, Swansea, Wales, SA2 8PP, UK.ORCID https://orcid.org/0000-0001-7262-0215
Farhan Saeed VakaniDow Institute of Health Professionals Education, Dow University of Health Sciences, Karachi, Sindh, 74200, Pakistan.ORCID https://orcid.org/0000-0002-6215-5492

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Generative artificial intelligence (GenAI) has rapidly become accessible to health professions educators and learners, creating both opportunities and new responsibilities for faculty development (FD). GenAI can help draft, adapt, and translate educational materials, and can assist faculty developers as a "co-developer." However, GenAI outputs require human oversight, and FD leaders must actively manage accuracy, privacy/confidentiality, equity/fairness, and sustainability concerns. This article offers a perspective on how GenAI can be leveraged as a co-developer in FD by drawing on emerging literature and discussion points from a workshop at the Applied insights: We provide eight applied insights, organised across key phases of FD work (planning, content creation, delivery, and evaluation). These insights highlight how faculty developers can: Conclusions: When used responsibly, GenAI can augment FD and help educators work more efficiently without reducing the central role of human judgment, ethics, and contextual expertise. Faculty developers can increase benefits and reduce harms by making their governance explicit, modelling prompt literacy, and embedding quality assurance/quality improvement cycles.

Indexed as

EquityEthicsFaculty developmentGenerative artificial intelligenceHealth professions educationTechnology-enhanced learning

Identifiers

PMID41552409
PMCPMC12811717

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.