Health Literacy and Acceptance of AI/XR-Enabled Telemedicine Among Romanian Medical Students: A Cross-Sectional Survey

<b>Background and Objectives</b>: AI- and extended reality (XR)-enabled telemedicine is increasingly relevant to clinical training, yet evidence from Central and Eastern Europe is limited. We assessed Romanian medical students' acceptance of AI/XR-enabled telemedicine and examined whether health literacy moderates the association between AI/XR knowledge and acceptance. <b>Methods</b>: We conducted an anonymous cross-sectional online survey of 212 medical students (years 1-6) at a single Romanian university (March 2024-June 2025). Acceptance was measured using a study-specific Acceptance Index (mean of three 4-point items: trust in AI-assisted recommendations, perceived improvement in telemedicine quality with AI/XR, and willingness to participate in AI/XR-enabled teleconsultations; internal consistency acceptable, Cronbach's α ≈ 0.8). Health literacy was assessed with the validated Romanian version of the European Health Literacy Survey Questionnaire (HLS-EU-Q16). We performed group comparisons, Spearman correlations, multivariable and hierarchical regression with a Knowledge × Health Literacy interaction, and k-means clustering. <b>Results</b>: Participants had a mean age of 22.5 ± 1.9 years; 66.0% were female. Overall acceptance was high (2.9 ± 0.6). Acceptance was higher in clinical vs. preclinical years (3.1 ± 0.6 vs. 2.8 ± 0.5; <i>p</i> < 0.001; Cohen's d ≈ 0.55) and in prior AI/XR users vs. non-users (3.2 ± 0.5 vs. 2.7 ± 0.6; <i>p</i> < 0.001; d ≈ 0.89). Knowledge correlated strongly with acceptance (ρ = 0.68; <i>p</i> < 0.001). In multivariable models (R<sup>2</sup> = 0.61), knowledge, perceived educational value, prior AI/XR use, and clinical stage independently predicted acceptance, whereas privacy concern and gender did not. Health literacy was sufficient in 64.2% and significantly moderated the knowledge-acceptance link (interaction <i>p</i> = 0.012). <b>Conclusions</b>: Romanian medical students report favorable acceptance of AI/XR-enabled telemedicine. Findings support curriculum integration that combines structured AI/XR teaching with literacy-sensitive scaffolding to ensure knowledge translates into informed, critical acceptance across student subgroups.

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Journal
Healthcare
Published
2026-02-25
DOI
https://doi.org/10.3390/healthcare14050570
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Health Literacy and Acceptance of AI/XR-Enabled Telemedicine Among Romanian Medical Students: A Cross-Sectional Survey

Adriana Laura Cojocaru, L. Nussbaum, Codrina Mihaela Levai, Iulius Jugănaru et al.
Healthcare
Artificial Intelligence in Healthcare and Education
article

Health Literacy and Acceptance of AI/XR-Enabled Telemedicine Among Romanian Medical Students: A Cross-Sectional Survey

Adriana Laura Cojocaru, L. Nussbaum, Codrina Mihaela Levai, Iulius Jugănaru, Daian-Ionel Popa, Andrei Marius Tomescu
article en

Abstract

<b>Background and Objectives</b>: AI- and extended reality (XR)-enabled telemedicine is increasingly relevant to clinical training, yet evidence from Central and Eastern Europe is limited. We assessed Romanian medical students' acceptance of AI/XR-enabled telemedicine and examined whether health literacy moderates the association between AI/XR knowledge and acceptance. <b>Methods</b>: We conducted an anonymous cross-sectional online survey of 212 medical students (years 1-6) at a single Romanian university (March 2024-June 2025). Acceptance was measured using a study-specific Acceptance Index (mean of three 4-point items: trust in AI-assisted recommendations, perceived improvement in telemedicine quality with AI/XR, and willingness to participate in AI/XR-enabled teleconsultations; internal consistency acceptable, Cronbach's α ≈ 0.8). Health literacy was assessed with the validated Romanian version of the European Health Literacy Survey Questionnaire (HLS-EU-Q16). We performed group comparisons, Spearman correlations, multivariable and hierarchical regression with a Knowledge × Health Literacy interaction, and k-means clustering. <b>Results</b>: Participants had a mean age of 22.5 ± 1.9 years; 66.0% were female. Overall acceptance was high (2.9 ± 0.6). Acceptance was higher in clinical vs. preclinical years (3.1 ± 0.6 vs. 2.8 ± 0.5; <i>p</i> < 0.001; Cohen's d ≈ 0.55) and in prior AI/XR users vs. non-users (3.2 ± 0.5 vs. 2.7 ± 0.6; <i>p</i> < 0.001; d ≈ 0.89). Knowledge correlated strongly with acceptance (ρ = 0.68; <i>p</i> < 0.001). In multivariable models (R<sup>2</sup> = 0.61), knowledge, perceived educational value, prior AI/XR use, and clinical stage independently predicted acceptance, whereas privacy concern and gender did not. Health literacy was sufficient in 64.2% and significantly moderated the knowledge-acceptance link (interaction <i>p</i> = 0.012). <b>Conclusions</b>: Romanian medical students report favorable acceptance of AI/XR-enabled telemedicine. Findings support curriculum integration that combines structured AI/XR teaching with literacy-sensitive scaffolding to ensure knowledge translates into informed, critical acceptance across student subgroups.

HealthcareVol. 14(5)
Victor Babeș University of Medicine and Pharmacy Timișoara (RO)
Quality Education
Openalex Percentile: Top 45%
Artificial Intelligence in Healthcare and Education
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