AI Reliance vs. Verification: A New Risk in Healthcare Education
Abstract
Background: Artificial Intelligence (AI) is rapidly reshaping Public Health, yet the readiness of future healthcare professionals to use it safely remains uncertain.
Objectives: This study investigates AI usage among future healthcare professionals and the AI-related educational needs of future professionals to inform academic curricula. A cross-sectional survey was conducted among 180 university students (77M, 103F; mean age 21.4±4.26) enrolled in Healthcare Professions degree programs. The survey assessed AI usage frequency, application areas, error detection, and perceived risks/benefits. It integrated two validated psychometric tools: the General Attitudes toward Artificial Intelligence Scale (GAAIS) and the AI Acceptance Scale for Interprofessional Education (AAIPE).
Results: AI adoption is high: 60.6% use it often, 36.1% sometimes, and only 3.3% do not use it. Over 80% rely on free versions. Notably, 79.5% utilize AI to seek health, wellness and medication information. Perceived benefits include expanded information access (60%), reduced wait times (44%), and diagnostic support (41%). Conversely, primary concerns are the erosion of the doctor-patient relationship (50%) and technological overreliance (45%). In this scenario of widespread use, a critical issue is highlighted: 86% of the respondents have detected errors in chatbot responses, despite this, only 27.2% always verify the accuracy of the information, while 58.9% do so only ‘sometimes’. A clear paradox emerged: despite using AI for clinical and pharmaceutical queries without rigorous verification, 56.1% believe AI will exacerbate health misinformation in future.
Conclusions: The gap between intensive AI usage, high error detection, and inconsistent source verification exposes future professionals to data inaccuracies. This paradox—fearing misinformation while lacking systematic critical filters—highlights an urgent Public Health priority: integrating AI literacy into academic curricula to safely govern AI.
Key messages: • High AI adoption without rigorous critical filters highlights an urgent public health priority: integrating AI literacy into core academic curricula to safely govern future medical practice.
• High AI reliance among healthcare students is coupled with low source verification rates, creating a direct pathway for severe medical misinformation in clinical settings.
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BibTeX
@article{mea2026reliance,
title = {{AI Reliance vs. Verification: A New Risk in Healthcare Education}},
author = {A Mea and G Moccia and M Tomeo and F Della Rocca and M Baldi and G D’Arienzo and E Santoro and G Boccia and M Capunzo and F De Caro},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1215},
url = {https://doi.org/10.1093/eurpub/ckag169.1215},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - AI Reliance vs. Verification: A New Risk in Healthcare Education
AU - A Mea
AU - G Moccia
AU - M Tomeo
AU - F Della Rocca
AU - M Baldi
AU - G D’Arienzo
AU - E Santoro
AU - G Boccia
AU - M Capunzo
AU - F De Caro
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1215
UR - https://doi.org/10.1093/eurpub/ckag169.1215
AB - Background: Artificial Intelligence (AI) is rapidly reshaping Public Health, yet the readiness of future healthcare professionals to use it safely remains uncertain. Objectives: This study investigates AI usage among future healthcare professionals and the AI-related educational needs of future professionals to inform academic curricula. A cross-sectional survey was conducted among 180 university students (77M, 103F; mean age 21.4±4.26) enrolled in Healthcare Professions degree programs. The survey assessed AI usage frequency, application areas, error detection, and perceived risks/benefits. It integrated two validated psychometric tools: the General Attitudes toward Artificial Intelligence Scale (GAAIS) and the AI Acceptance Scale for Interprofessional Education (AAIPE). Results: AI adoption is high: 60.6% use it often, 36.1% sometimes, and only 3.3% do not use it. Over 80% rely on free versions. Notably, 79.5% utilize AI to seek health, wellness and medication information. Perceived benefits include expanded information access (60%), reduced wait times (44%), and diagnostic support (41%). Conversely, primary concerns are the erosion of the doctor-patient relationship (50%) and technological overreliance (45%). In this scenario of widespread use, a critical issue is highlighted: 86% of the respondents have detected errors in chatbot responses, despite this, only 27.2% always verify the accuracy of the information, while 58.9% do so only ‘sometimes’. A clear paradox emerged: despite using AI for clinical and pharmaceutical queries without rigorous verification, 56.1% believe AI will exacerbate health misinformation in future. Conclusions: The gap between intensive AI usage, high error detection, and inconsistent source verification exposes future professionals to data inaccuracies. This paradox—fearing misinformation while lacking systematic critical filters—highlights an urgent Public Health priority: integrating AI literacy into academic curricula to safely govern AI. Key messages: • High AI adoption without rigorous critical filters highlights an urgent public health priority: integrating AI literacy into core academic curricula to safely govern future medical practice. • High AI reliance among healthcare students is coupled with low source verification rates, creating a direct pathway for severe medical misinformation in clinical settings.
SN - 1101-1262
SN - 1464-360X
ER -