Evidence map›Paper›PMID 41584941›Full record

ArticleCanadian medical education journal2025

OSCEai: personalized interactive learning for undergraduate medical education.

Eddie Guo, Rashi Ramchandani, Ye-Jean Park, Mehul Gupta

Abstract read
In one paragraph

Article in Canadian medical education journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. AI in health professions education.Canadian medical education journal · 2025
    Article
  4. Article
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

4 authors.

Eddie GuoCumming School of Medicine, University of Calgary, Alberta, Canada.
Rashi RamchandaniFaculty of Medicine, University of Ottawa, Ontario, Canada.
Ye-Jean ParkTemerty Faculty of Medicine, University of Toronto, Ontario, Canada.
Mehul GuptaDepartment of Internal Medicine, University of Calgary, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aims to evaluate the effectiveness of the OSCEai, a large language model-based platform that simulates clinical encounters, in enhancing undergraduate medical education. Methods: A web-based application, OSCEai, was developed to bridge theoretical and practical learning. Following use, medical students from the University of Calgary Class of 2026 completed an anonymized survey on the usability, utility, and overall experience of OSCEai. Results: A total of 37 respondents answered the anonymized survey. The OSCEai platform was highly valued for its ability to provide data on demand (33/37), support self-paced learning (30/37), and offer realistic patient interactions (29/37). The ease of use and medical content quality were rated at 4.73 (95% CI: 4.58 to 4.88) and 4.70 (95% CI: 4.55 to 4.86) out of 5, respectively. Some participants (8/37) commented that few cases were not representative and needed clarification about app functionality. Despite these limitations, OSCEai was favorably compared to lecture-based teaching methods, with an overall reception rating of 4.62 (95% CI: 4.46 to 4.79) out of 5. Interpretation: The OSCEai platform fills a gap in medical training through its scalable, interactive, and personalized design. The findings suggest that integrating technologies, like OSCEai, into medical curricula can enhance the quality and efficacy of medical education.

Indexed as

Computer-Assisted InstructionEducational MeasurementEducation, Medical, UndergraduateClinical CompetenceHumansInternetLarge Language ModelsLearningStudents, MedicalSurveys and Questionnaires

Identifiers

PMID41584941
PMCPMC12826823

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.