Evidence map›Paper›PMID 37261894›Full record

ArticleJMIR medical education2023

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

Alaa Abd-Alrazaq, Rawan AlSaad, Dari Alhuwail, Arfan Ahmed, Padraig Mark Healy, Syed Latifi, Sarah Aziz, Rafat Damseh, Sadam Alabed Alrazak, Javaid Sheikh

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07281066 (Evaluating ChatGPT-4o, Gemini and Claude 3.7 in Endodontic Diagnostics), which is not on this map. Cited by 275 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
275citing papers in PubMed, 11 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.

NCT07281066 completednot on this mapstarted 2025, after this paper: background citation

Evaluating ChatGPT-4o, Gemini and Claude 3.7 in Endodontic Diagnostics: A Prospective Clinical Study

TypeobservationalSponsorMarmara UniversityRan2025 to 2025Enrolled120ConditionsEndodontic Diagnosis, Endodontic Diseases, Endodontic Treatment, Endodontic Decision-makingArmsAI-Based Diagnostic Assessment
3 · Its place in the literature

Who cites it

275 citing papers in PubMed, 11 syntheses or guidelines pooled it.

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215 more citing papers are in PubMed but not listed here.

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

10 authors.

Alaa Abd-AlrazaqAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0001-7695-4626
Rawan AlSaadAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0002-3235-0860
Dari AlhuwailInformation Science Department, College of Life Sciences, Kuwait University, Kuwait, Kuwait.ORCID https://orcid.org/0000-0001-5038-3044
Arfan AhmedAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0002-4025-5767
Padraig Mark HealyOffice of Educational Development, Division of Medical Education, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0002-5804-0342
Syed LatifiOffice of Educational Development, Division of Medical Education, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0003-0505-609X
Sarah AzizAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0002-0861-9743
Rafat DamsehDepartment of Computer Science and Software Engineering, United Arab Emirates University, Abu Dhabi, United Arab Emirates.ORCID https://orcid.org/0000-0001-6797-0448
Sadam Alabed AlrazakDepartment of Mechanical & Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-8586-7564
Javaid SheikhAI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.ORCID https://orcid.org/0000-0002-5762-4186

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)-driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.

Indexed as

artificial intelligenceChatGPTeducatorsgenerative AIGPT-4large language modelsmedical educationstudents

Identifiers

PMID37261894
PMCPMC10273039

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.