Evidence map›Paper›PMID 41709652›Full record

ArticleInquiry : a journal of medical care organization, provision and financing

Medical Students' Readiness for Medical Artificial Intelligence (AI).

Albena Gayef, Gökhan Ilgaz

Abstract read
In one paragraph

Article in Inquiry : a journal of medical care organization, provision and financing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Albena GayefTrakya University Faculty of Medicine Department of Medical Education, Edirne, Türkiye.ORCID 0000-0002-1260-0631
Gökhan IlgazTrakya University Faculty of Education Educational Sciences Department, Edirne, Türkiye.ORCID 0000-0001-8988-5279

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence offers students more personalised and adaptive learning, which encourages educators to better understand students' learning processes. This study aims to determine the readiness levels of medical faculty students in medical artificial intelligence and to examine whether these levels vary based on gender and class year. The study was conducted with 322 medical students. Research data were collected using the "Medical Artificial Intelligence Readiness Scale for Medical Students." Results showed that medical students rated themselves as moderate in the "cognition" and "vision" dimensions, slightly higher in the "ability" and "ethics" dimensions, and overall at a "neutral" level in medical artificial intelligence readiness. Compared to females, males showed significant differences at a "small effect" level in cognition, ability factors and overall scores. Regarding class levels, significant differences were found between 2nd graders and both 5th and 6th graders, favouring the 2nd graders at an "intermediate effect" level. In the cognition dimension, there was also a significant difference between the 2nd and 4th grades in favour of the 2nd grade and at the level of "small effect." In order to increase medical artificial intelligence readiness of students, it is important to comprehensively include the subject in the medical school curriculum and to develop it according to needs. In future research, long-term follow-up studies aimed at improving medical students' education in the field of medical artificial intelligence (AI) are considered to be very beneficial. Furthermore, future studies should also consider potential changes in medical AI readiness that may occur over time.

Indexed as

Artificial IntelligenceStudents, MedicalCognitionFemaleHumansMaleSex FactorsYoung Adultartificialintelligencemedicalreadinessstudent

Identifiers

PMID41709652
PMCPMC12924919

What OpenQuestion holds

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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.