File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Situating governance and regulatory concerns for generative artificial intelligence and large language models in medical education

TitleSituating governance and regulatory concerns for generative artificial intelligence and large language models in medical education
Authors
Tran, MichaelBalasooriya, ChinthakaJonnagaddala, JitendraLeung, Gilberto Ka-KitMahboobani, NeerajRamani, SubhaRhee, JoelSchuwirth, LambertNajafzadeh-Tabrizi, Neysan SedaghatSemmler, CarolynWong, Zoie SY
Issue Date27-May-2025
PublisherNature Research
Citation
npj Digital Medicine, 2025, v. 8, p. 1-10 How to Cite?
DOI: http://dx.doi.org/10.1038/s41746-025-01721-z
Abstract

Generative artificial intelligence (GenAI) and large language models represent gains in educational efficiency and personalisation of learning. These are balanced against the considerations of the learning process, authentic assessment, and academic integrity. A pedagogical approach helps situate these concerns, and informs various types of governance and regulatory approaches. In this review we identify current and emerging issues regarding GenAI in medical education including pedagogical considerations, emerging roles, and trustworthiness. Potential measures to address specific regulatory concerns are explored.


Persistent Identifierhttp://hdl.handle.net/10722/356544
ISSN
2398-6352
2023 Impact Factor: 12.4
2023 SCImago Journal Rankings: 4.273
ISI Accession Number ID
WOS:001497735200002

 

DC FieldValueLanguage
dc.contributor.authorTran, Michael-
dc.contributor.authorBalasooriya, Chinthaka-
dc.contributor.authorJonnagaddala, Jitendra-
dc.contributor.authorLeung, Gilberto Ka-Kit-
dc.contributor.authorMahboobani, Neeraj-
dc.contributor.authorRamani, Subha-
dc.contributor.authorRhee, Joel-
dc.contributor.authorSchuwirth, Lambert-
dc.contributor.authorNajafzadeh-Tabrizi, Neysan Sedaghat-
dc.contributor.authorSemmler, Carolyn-
dc.contributor.authorWong, Zoie SY-
dc.date.accessioned2025-06-04T00:40:20Z-
dc.date.available2025-06-04T00:40:20Z-
dc.date.issued2025-05-27-
dc.identifier.citationnpj Digital Medicine, 2025, v. 8, p. 1-10-
dc.identifier.issn2398-6352-
dc.identifier.urihttp://hdl.handle.net/10722/356544-
dc.description.abstract<p>Generative artificial intelligence (GenAI) and large language models represent gains in educational efficiency and personalisation of learning. These are balanced against the considerations of the learning process, authentic assessment, and academic integrity. A pedagogical approach helps situate these concerns, and informs various types of governance and regulatory approaches. In this review we identify current and emerging issues regarding GenAI in medical education including pedagogical considerations, emerging roles, and trustworthiness. Potential measures to address specific regulatory concerns are explored.<br></p>-
dc.languageeng-
dc.publisherNature Research-
dc.relation.ispartofnpj Digital Medicine-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleSituating governance and regulatory concerns for generative artificial intelligence and large language models in medical education-
dc.typeArticle-
dc.identifier.doi10.1038/s41746-025-01721-z-
dc.identifier.volume8-
dc.identifier.spage1-
dc.identifier.epage10-
dc.identifier.eissn2398-6352-
dc.identifier.isiWOS:001497735200002-
dc.identifier.issnl2398-6352-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats