Evidence map›Paper›PMID 38145370›Full record

ReviewDiagnostic and interventional radiology (Ankara, Turkey)2024

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources

İsmail Meşe, Ceylan Altıntaş Taşlıçay, Beyza Nur Kuzan, Taha Yusuf Kuzan, Ali Kemal Sivrioğlu

Open access · diamondAbstract readComparative StudyReview
In one paragraph

Review in Diagnostic and interventional radiology (Ankara, Turkey), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.9field-weighted citation impact, top 19% of its field
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

10 citing papers in PubMed, 24 citations in OpenAlex.

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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 at 1 institution in 4 countries.

İsmail MeşeUniversity of Health Sciences Türkiye, Erenköy Mental Health and Neurology Training and Research Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0002-4429-6996
Ceylan Altıntaş TaşlıçayMD Anderson Cancer Center, Department of Radiology, Houston, Texas, United StatesORCID 0000-0003-4459-4114
Beyza Nur KuzanKartal Dr. Lütfi Kırdar City Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0002-5001-3649
Taha Yusuf KuzanSancaktepe Şehit Prof. Dr. İlhan Varank Training and Research Hospital, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0002-5420-8507
Ali Kemal SivrioğluLiv Hospital Vadistanbul, Clinic of Radiology, İstanbul, TürkiyeORCID 0000-0002-0500-6335
The University of Texas MD Anderson Cancer Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

Indexed as

Artificial IntelligenceComputer-Assisted InstructionRadiologistsRadiologyHumansInternship and ResidencyArtificial intelligenceChatGPTdigital case studieseducational videosradiology education

Identifiers

PMID38145370
PMCPMC11095068
OpenAlexW4390193237

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.