Medical Students' Attitudes, Perceptions, and Self-Reported Familiarity With AI in Health Care: Systematic Review and Meta-Analysis.

Abstract

Background: AI is increasingly encountered in clinical care and medical education, but medical students' attitudes, perceptions, and self-reported familiarity have been assessed using heterogeneous survey instruments, AI referents, and response scales. Prior reviews often combined mixed health profession populations or summarized central estimates without fully showing variation across settings.

Objective: This study aimed to synthesize quantitative evidence on medical students' AI-related attitudes, perceptions, and self-reported familiarity while examining construct harmonization, participant independence, heterogeneity, prediction intervals, risk of bias, and certainty of evidence.

Methods: We searched PubMed (MEDLINE), Embase, Web of Science, Scopus, PsycINFO, and Cochrane CENTRAL from inception to April 1, 2026; supplementary searches are described in the appendices. Eligible studies enrolled students in MD, MBBS, MBChB, or DO-equivalent medical programs, or reported separable medical student data from mixed samples. Proportion outcomes were harmonized into 9 domains and synthesized using random-effects meta-analysis with Freeman-Tukey double-arcsine transformation, Hartung-Knapp-Sidik-Jonkman-adjusted CIs, and prediction intervals. Subgroup analyses and meta-regressions were exploratory because of multiple testing, ecological confounding, and construct heterogeneity. Risk of bias and certainty were assessed using the Joanna Briggs Institute analytical cross-sectional checklist and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) framework, respectively.

Results: Ninety-six cross-sectional studies from 37 countries (>45,000 medical students) were included. Summary estimates suggested favorable attitudes but wide between-setting dispersion. Positive attitude toward AI was 76.9% (95% CI 72.2%-81.4%; prediction interval 42.2%-98.3%; I²=98.3%; 44 studies; N=20,806), perceived career benefit was 78.4% (95% CI 69.5%-86.2%; prediction interval 45.3%-98.3%; I²=98.0%; 16 studies; N=9799), and support for curricular integration was 76.6% (95% CI 71.8%-81.1%; prediction interval 47.8%-96.1%; I²=97.2%; 38 studies; N=16,308). Concern about physician replacement was 39.9% (95% CI 33.6%-46.5%; prediction interval 6.6%-80.1%; I²=98.8%; 32 studies; N=16,642), willingness to learn about or adopt AI was 71.5% (95% CI 64.8%-77.8%; prediction interval 37.9%-95.4%; I²=97.7%; 22 studies; N=9199), and ethical concerns were endorsed by 62.8% (95% CI 53.9%-71.3%; prediction interval 21.9%-95.0%; I²=98.8%; 28 studies; N=14,571). Self-reported familiarity or knowledge was 63.3% (95% CI 55.9%-70.3%; prediction interval 7.8%-100.0%; I²=99.5%; 52 studies; N=27,817), and trust in AI-assisted decisions was 50.6% (95% CI 28.5%-72.6%; prediction interval 7.3%-93.3%; I²=98.1%; 8 studies; N=3007). All domains had very low certainty because of cross-sectional self-report designs, frequent use of nonvalidated or adapted instruments, wide prediction intervals, and small study effects in several domains.

Conclusions: Medical students' AI-related attitudes and curricular interest appear broadly favorable, but these estimates should not be interpreted as stable global prevalences. This review adds value by restricting the population to medical students, transparently harmonizing nonequivalent constructs, auditing mixed populations and participant independence, and reporting prediction intervals and certainty. Given very low certainty, the findings support locally adapted, exploratory AI-literacy planning and standardized measurement in future studies rather than strong claims about curriculum effectiveness.