Evidence map›Paper›PMID 33602196›Full record

ArticleBMC medical education2021

Medical artificial intelligence readiness scale for medical students (MAIRS-MS) - development, validity and reliability study.

Ozan Karaca, S Ayhan Çalışkan, Kadir Demir

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Article in BMC medical education, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 4 pooled it
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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

117 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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57 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

3 authors.

Ozan KaracaDepartment of Medical Education, Ege University Faculty of Medicine, İzmir, Turkey.
S Ayhan ÇalışkanDepartment of Medical Education, Ege University Faculty of Medicine, İzmir, Turkey. ayhan.caliskan@ege.edu.tr.
Kadir DemirDepartment of Computer Education and Instructional Technology, Dokuz Eylül University Buca Faculty of Education, İzmir, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine.

methodsTo define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied.

resultsA total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (χ

conclusionsThe newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.

Indexed as

Students, MedicalArtificial IntelligenceHumansPsychometricsReproducibility of ResultsSurveys and QuestionnairesTurkeyArtificial intelligenceMedical educationMedical studentsMedicineReadinessScale developmentValidity and reliability

Identifiers

PMID33602196
PMCPMC7890640

What OpenQuestion holds

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LicenceCC BY
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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.