Evidence map›Paper›PMID 41181167›Full record

ArticleJournal of medical education and curricular development

Perceptions and Use of Generative Artificial Intelligence in Medical Students: A Multicenter Survey.

Cecilia Tran, Brett N Hryciw, Sean William Moore, Alan Chaput, Andrew John Ervine Seely

Abstract read
In one paragraph

Article in Journal of medical education and curricular development. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 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

12 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

5 authors.

Cecilia TranDepartment of Medicine, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.ORCID https://orcid.org/0009-0003-2232-1630
Brett N HryciwDepartment of Critical Care, The Ottawa Hospital, Ottawa, Ontario, Canada.
Sean William MooreEmergency Medicine Section, Division of Clinical Sciences, Northern Ontario School of Medicine University, Thunder Bay, Ontario, Canada.ORCID https://orcid.org/0000-0001-7059-6936
Alan ChaputDepartment of Anesthesiology and Pain Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada.
Andrew John Ervine SeelyDivision of Thoracic Surgery, Department of Surgery, The Ottawa Hospital, Ottawa, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Generative artificial intelligence (AI) has transformative potential in medical training, and its role in medicine holds drastic implications for patients, healthcare providers, and society; however, its current use by medical students is unknown. The study aims to characterize the use, frequency of use, and perceptions of generative AI by Canadian medical students. Methods: A cross-sectional survey was distributed to 6 medical schools in Ontario, Canada, to investigate how medical students use generative AI in education, clinical settings, and for communication, and to assess the perceived barriers and enablers that influence their use. Results: A total of 167 respondents completed the survey (60.8% female, 69.3% in first and second year), and over 78.9% of respondents reported using generative AI, with ChatGPT being the most popular model; 53.0% of respondents were frequent users and reported using generative AI tools at least once a week. In clinical settings, students report using generative AI for learning and reviewing medical content, summarizing clinical guidelines, and generating differential diagnoses; 92.8% of students were willing to learn how to use generative AI to integrate it into their future clinical practice. At the same time, most medical students appreciated the limitations of generative AI in terms of its risk for inaccuracy (91.6%) and bias (78.9%); 75.9% of participants agreed that generative AI should be implemented as a resource or formal teaching topic in medical training. Discussion: The findings of this study may help guide medical education institutions in adapting curricula and developing policies to promote the ethical and appropriate use of generative AI in medicine.

Indexed as

artificial intelligencelarge language modelsmedical educationsurveyundergraduate training

Identifiers

PMID41181167
PMCPMC12576227

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.