Evidence map›Paper›PMID 40281510›Full record

ArticleBMC medical education2025

The role of artificial intelligence in medical education: an evaluation of Large Language Models (LLMs) on the Turkish Medical Specialty Training Entrance Exam.

Murat Koçak, Ali Kemal Oğuz, Zafer Akçalı

Abstract read
In one paragraph

Article in BMC medical education, 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
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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

3 authors.

Murat KoçakDepartment of Medical Informatics, Faculty of Medicine, Baskent University, Ankara, Turkey. muratkocak25@gmail.com.ORCID http://orcid.org/0000-0001-6510-3666
Ali Kemal OğuzDepartment of Internal Medicine, Faculty of Medicine, Baskent University, Ankara, Turkey.ORCID http://orcid.org/0000-0002-3939-2924
Zafer AkçalıDepartment of Medical Informatics, Faculty of Medicine, Baskent University, Ankara, Turkey.ORCID http://orcid.org/0000-0003-2473-4431

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the performance of advanced large language models (LLMs)-OpenAI-ChatGPT 4, Google AI-Gemini 1.5 Pro, Cohere-Command R + and Meta AI-Llama 3 70B on questions from the Turkish Medical Specialty Training Entrance Exam (2021, 1st semester) and analyze their answers for user interpretability in languages other than English.

methodsThe study used questions from the Basic Medical Sciences and Clinical Medical Sciences exams of the Turkish Medical Specialty Training Entrance Exam held on March 21, 2021. The 240 questions were presented to the LLMs in Turkish, and their responses were evaluated based on the official answers published by the Student Selection and Placement Centre.

resultsChatGPT 4 was the best-performing model with an overall accuracy of 88.75%. Llama 3 70B followed closely with 79.17% accuracy. Gemini 1.5 Pro achieved 78.13% accuracy, while Command R + lagged with 50% accuracy. ChatGPT 4 demonstrated strengths in both basic and clinical medical science questions. Performance varied across question difficulties, with ChatGPT 4 maintaining high accuracy even on the most challenging questions.

conclusionsGPT-4 and Llama 3 70B achieved satisfactory results on the Turkish Medical Specialty Training Entrance Exam, demonstrating their potential as safe sources for basic medical sciences and clinical medical sciences knowledge in languages other than English. These LLMs could be valuable resources for medical education and clinical support in non-English speaking areas. However, Gemini 1.5 Pro and Command R + show potential but need significant improvement to compete with the best-performing models.

Indexed as

Artificial IntelligenceEducational MeasurementEducation, MedicalHumansLarge Language ModelsTurkeyAILLMsMedical educationTurkish medical specialty training entrance exam

Identifiers

PMID40281510
PMCPMC12023555

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Registered trials

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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.