Perceptions, effectiveness, and credibility of artificial intelligence in healthcare among medical students and interns: A cross-sectional study
Paper Details
Perceptions, effectiveness, and credibility of artificial intelligence in healthcare among medical students and interns: A cross-sectional study
Abstract
Artificial intelligence (AI) is rapidly transforming healthcare and medical education; however, its successful integration depends on the perceptions, perceived effectiveness, and credibility among future healthcare professionals. This study aimed to evaluate these dimensions among medical students and interns. A cross-sectional descriptive study was conducted among 151 participants using a structured, self-administered questionnaire. The instrument included demographic variables and Likert-scale items assessing perception, effectiveness, and credibility of AI. Data were analyzed using descriptive statistics; continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Likert-scale responses were summarized and reported in tabular form. The mean age of participants was 23.9 ± 2.8 years; 82.8% were medical students and 64.9% were male. Overall, participants demonstrated a positive perception of AI. A large majority agreed that AI improves diagnostic accuracy (86.1%), enhances patient outcomes (86.1%), supports treatment decisions (84.8%), and reduces medical errors (78.1%). Strong support was observed for integrating AI into the medical curriculum (82.1%), and 86.7% recognized its benefits in disease management. Perceived effectiveness was consistently high across domains. However, credibility perceptions were comparatively moderate. While 62.9% trusted AI-based clinical decision-making, 80.1% considered AI reliable only with physician supervision, and 86.1% emphasized the need for human verification. Willingness to rely on AI for critical decisions was lower (52.3%). In conclusion, medical students and interns show strong acceptance of AI effectiveness, alongside conditional trust in its credibility, highlighting the need for structured education and supervised implementation.
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Copyright © 2026 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.