Insights Into the Future: Assessing Medical Students' Artificial Intelligence Readiness ‐ A Cross‐Sectional Study at Kerman University of Medical Sciences (2022)

Abstract

ABSTRACT Background Artificial intelligence (AI) has recently advanced in medicine globally, transforming healthcare delivery and medical education. While AI integration into medical curricula is gaining momentum worldwide, research on medical students' preparedness remains limited, particularly in developing countries. This paper aims to investigate the readiness of medical students at the Kerman University of Medical Sciences to employ AI in medicine in 2022. Methods This cross‐sectional research was carried out by distributing the validated 20‐item Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS‐MS) among 360 medical students, with a response rate of 94% ( n = 340). The MAIRS‐MS assessed four domains, including cognition (8 items), ability (7 items), vision (2 items), and ethics (3 items), using a 5‐point Likert scale. Data analysis was conducted by descriptive statistics and independent sample t ‐tests in SPSS v24.0, considering p < 0.05 significant. Results Participants demonstrated below‐average readiness scores across all domains: ability ( M = 21.88 ± 6.74, 62.5% of the maximum possible score), cognition ( M = 20.30 ± 7.04, 50.8%), ethics ( M = 10.94 ± 3.04, 72.9%), and vision ( M = 6.09 ± 1.94, 60.9%). The total mean readiness score was 59.21 ± 16.12 (59.2% of the maximum). The highest and lowest‐rated items were “value of AI in education” (3.96 ± 1.18) and “explaining AI system training” (2.10 ± 1.01), respectively. No significant differences were found across demographic factors ( p > 0.05). Conclusion Iranian medical students currently show limited readiness for AI integration in healthcare practice. Therefore, the study recommends: (1) implementing structured introductory AI courses in medical curricula, focusing particularly on technical fundamentals and practical applications, and (2) developing hands‐on training programs that combine AI concepts with clinical scenarios. These findings provide valuable insights for curriculum development and educational policy in medical education.

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