Evidence map›Paper›PMID 42210274›Full record

ArticleBMC medical education2026

Identifying necessary conditions for medical students' adoption of AI in the future practice: a survey study in Canada.

Mickaël Ringeval, Louis Raymond, Guy Paré

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mickaël RingevalDepartment of Computer Information Systems, Bentley University, 175 Forest Street, Waltham, MA, 02452, USA. mringeval@bentley.edu.
Louis RaymondUniversité du Québec à Trois-Rivières, Trois-Rivières, Canada.
Guy ParéHEC Montréal, Montreal, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs artificial intelligence (AI) technologies become increasingly embedded in healthcare, the readiness of future physicians to adopt these tools is of growing concern. While prior studies have examined predictors of technology acceptance, less is known about the conditions that are essential, rather than merely influential, for medical students to form strong intentions to use AI-based health technologies (AIHTs) in clinical practice. This study addresses this gap by identifying both necessary and sufficient conditions for AI adoption intentions among medical students.

methodsDrawing on the Consolidated Framework for Implementation Research and the COM-B model (Capabilities, Opportunities, Motivation-Behavior), the study surveyed 177 students at a Canadian medical school. Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) were applied to explore how students' familiarity with AIHTs, hands-on experimentation, perceived curricular importance, and beliefs about AI's role in future medical tasks influence their adoption intentions.

resultsThe findings reveal that two conditions are necessary for strong AI adoption intentions: (1) a belief in the future relevance of AIHTs to medical tasks, and (2) a positive attitude toward the inclusion of AIHTs in the medical curriculum. The fsQCA further identifies two distinct "AI profiles" or configurations that are sufficient to foster strong adoption intentions, illustrating multiple pathways to readiness.

conclusionThese results highlight the importance of curricular design that not only builds technical familiarity but also fosters motivation and belief in AI's clinical relevance. The study offers practical insights for medical educators aiming to prepare students for a digitally integrated healthcare environment.

Indexed as

Artificial IntelligenceDigital HealthStudents, MedicalAdultAttitude of Health PersonnelAttitude to ComputersCanadaCurriculumFemaleHumansIntentionMaleMotivationSurveys and QuestionnairesYoung AdultArtificial intelligenceCurriculum designDigital healthfsQCAMedical educationNCATechnology adoption

Identifiers

PMID42210274
PMCPMC13277269

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.