Attitudes and Readiness of Medical Students in Iraq towards Artificial Intelligence: A Cross-Sectional Study.

Background Artificial Intelligence (AI) has emerged as a transformative force in healthcare, enhancing diagnostic precision, predicting outcomes, and optimizing administrative processes. In medical education, AI supports learning through simulation, automated assessments, and diagnostic training. However, successful implementation relies on clinicians’ and students’ readiness to adopt AI technologies. Objectives This study aims to assess the attitudes of Iraqi undergraduate medical students toward AI in healthcare and to evaluate their readiness across three key domains: ability, vision, and ethics for the integration of AI into medical education and clinical practice. Methods A cross-sectional study was conducted among 4th–6th-year medical students across Iraq from July 22 to September 1, 2025. Data were collected using a validated online questionnaire distributed via social media. The questionnaire consisted of three sections: (1) sociodemographic characteristics and AI exposure; (2) attitudes toward AI, using a modified version of the questionnaire developed by Pinto dos Santos et al.; and (3) readiness for AI integration, evaluated using a 14-item version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS), covering the domains of ability, vision, and ethics. Cronbach’s alpha values of 0.856, 0.793, 0.825, and 0.880 for the respective domains and overall scale. Data were analyzed using SPSS v26. Mann–Whitney U test and Chi-square test were used to compare groups, with statistical significance set at p < 0.05. Results A total of 862 students responded (mean age 22.65 ± 1.62 years; 50.2% male). Nearly two-thirds used AI in both academic and personal contexts, and 55.7% considered themselves technologically skilled. Overall, > 60% agreed that AI will revolutionize diagnostic specialties and medicine and should be included in medical curricula. Readiness analysis revealed high agreement in the ability domain (> 70%) and moderate agreement for vision (> 5%), with strong ethical awareness (> 65%). Male and technologically skilled students demonstrated significantly more positive attitudes and overall readiness (p < 0.05). Conclusion Iraqi medical students exhibited generally positive attitudes and moderate readiness toward AI integration in healthcare. Technological competence significantly influenced both attitude and readiness levels. The study emphasizes the need for tailored educational programs to enhance preparedness for the future of AI-driven medicine.