Readiness and Perceptions toward Artificial Intelligence among medical students in Egypt

Abstract Background Artificial intelligence (AI) is transforming healthcare, but medical students' readiness to adopt it remains unclear. Limited research exists on their awareness, skills, and perception toward AI in medicine. This study evaluates AI readiness and perceptions among medical students at Port Said University. Objective To assess Readiness towards Artificial Intelligence among medical students in Egypt. To assess Perception towards Artificial Intelligence among medical students in Egypt. Methods A cross-sectional study was conducted among medical students from 8 Egyptian Universities, selected through convenient sampling. The Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was used to assess students’ readiness. The Perception of Artificial Intelligence in Medical Students (PAIMS) scale was used to evaluate students’ perceptions of AI. Data were collected using online self-administered questionnaires to assess AI readiness and perceptions. Data analysis was performed using SPSS version 25 . Results A total of 356 responses were collected. The median total readiness score (MAIRS-MS) was 66.0 (IQR: 26.0). Among the readiness domains, Ability had the highest median score (24.0/40). Cognition scores varied significantly across years of study (p = 0.012) and among students who had attended an AI course (16%, p = 0.008). No significant differences in AI readiness scores were observed across universities. The median overall PAIMS score was 2.25 (IQR: 0.67), with the Knowledge and Trust domain having a median of 2.6 (IQR: 1). No significant differences in AI perceptions were observed across student characteristics. Conclusion Students from Egyptian universities demonstrated moderate readiness for AI integration, with strengths in practical ability and variable cognition influenced by academic year and prior AI training. Their perceptions of AI were generally positive and consistent across student groups. These findings can inform curriculum development by identifying areas where targeted AI education and training are needed, supporting the incorporation of AI tools into medical education, and preparing future physicians to effectively engage with AI in clinical practice.