Bridging the Gap: Assessing Medical Students Readiness for Artificial Intelligence in Healthcare

Background: Artificial Intelligence (AI) is transforming healthcare by improving diagnostics, treatment planning, and patient management. As AI continues to be integrated into clinical practice, future healthcare professionals must be adequately prepared to work alongside AI-driven systems. While previous studies suggest that medical students have a positive perception of AI, their actual readiness and competency remain unclear. Objective: This study aims to assess AI readiness among medical students using the Medical AI Readiness Scale for Medical Students (MAIRS-MS), while analyzing variations across gender and academic phases. Materials and Methods: A cross-sectional observational study was conducted among MBBS students at Government Medical College, Thrissur, Kerala, India. A total of 270 students participated, and their AI readiness was assessed using the MAIRS-MS questionnaire, which evaluates Cognition (AI Knowledge), Ability (AI Skills), Vision (AI Perception), and Ethics (Ethical Considerations). Independent-sample t tests and one-way analysis of variance were used to compare readiness scores across gender and academic years. Results: The mean AI readiness score was 58.39 ± 14.97 (out of 110), indicating moderate preparedness. Male students scored significantly higher than females (63.98 vs. 55.26, P < 0.001). The lowest subdomain score was Cognition (47.30 ± 14.81), highlighting gaps in AI-related knowledge. No significant differences were found in AI readiness across academic years ( P = 0.657). Pearson’s correlation analysis revealed that AI Ability ( r = 0.902, P < 0.001) had the strongest association with overall readiness. Conclusion: Medical students demonstrate moderate AI readiness, with notable gender disparities and knowledge gaps. The absence of significant differences across academic years suggests that AI education is not systematically integrated into medical curricula. These findings highlight the urgent need for structured AI education, ensuring that future physicians are competent and confident in AI-driven clinical practice.