Evidence map›Paper›PMID 40611183›Full record

ArticleBMC medical education2025

Enhancing AI literacy in undergraduate pre-medical education through student associations: an educational intervention.

Spencer Hopson, Carson Mildon, Kyle Hassard, Corbyn Kubalek, Lauren Laverty, Paul Urie, Dennis Della Corte

Abstract read
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

7 authors.

Spencer HopsonDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Carson MildonDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Kyle HassardDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Corbyn KubalekDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Lauren LavertyDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Paul UrieDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
Dennis Della CorteDepartment of Physics and Astronomy, Brigham Young University, Provo, UT, USA. dennis.dellacorte@byu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of artificial intelligence (AI) into healthcare is rapidly advancing, with profound implications for medical practice. However, a gap exists in formal AI education for pre-medical students. This study evaluates the effectiveness of the AI in Medicine Association (AIM), an extracurricular program designed to equip pre-medical students with foundational AI knowledge.

methodsA quasi-experimental pretest-posttest control group design was employed, comparing knowledge acquisition between students participating in the AIM program (cohort group) and a control group of students not participating. The intervention spanned four weeks and included hands-on AI training, ethical considerations, data preprocessing, and model evaluation. Pretest and posttest assessments measured AI knowledge and pathology-related skills.

resultsParticipants in the AIM program demonstrated significant improvements in both AI knowledge and pathology-related scores. The cohort group showed a large effect size across all measured domains, particularly in pathology, with Cohen's d values ranging from 1.83 to 4.74. Statistical analysis confirmed robust, significant improvements in test scores (t-test and Mann-Whitney U test, p < 0.001). There was no significant correlation between previous AI experience or attitudes toward AI and overall score improvement.

conclusionsThe AIM program effectively improved pre-medical students' understanding of AI and its application in medicine, particularly in pathology. This study highlights the potential of extracurricular programs to address the need for AI education in medical curricula, especially in the pre-medical phase, and suggests that such initiatives could serve as a model for other institutions seeking to integrate AI education into healthcare training.

Indexed as

Artificial IntelligenceEducation, Medical, UndergraduateEducation, PremedicalAdultCurriculumEducational MeasurementFemaleHumansMaleStudents, MedicalYoung AdultAI in medicineArtificial intelligence educationMedical AI curriculumPathology AI trainingPre-medical education

Identifiers

PMID40611183
PMCPMC12225195

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.