Hamadan University of Medical Sciences Journal of Education and Community Health 2820-896X 12 4 2025 12 31 Assessing the Readiness for Artificial Intelligence Integration Into Healthcare Among Medical Students in Sumatra, Indonesia 213 224 10.34172/jech.3495 EN Rizma Adlia Syakurah https://orcid.org/0000-0003-0633-6595 Meiliza Izzatika Muhimatul Mufarikhah Mariatul Fadilah Journal Article 10.34172/jech.3495 2025 06 17 2025 12 20 Introduction: Artificial Intelligence (AI) is increasingly applied in healthcare as it helps healthcare workers to improve the quality of care. Medical students should prepare themselves with the competence to properly and ethically use AI, especially in areas with socioeconomic challenges and limited educational resources. This study assessed medical students’ current readiness in healthcare in Sumatra. Methods: This cross-sectional study was conducted on medical students in Sumatra from November 2024 to February 2025. AI readiness was evaluated using the MAIRS-MS questionnaire. Data were analyzed using chi-square for bivariate tests and multivariate logistic regression to identify significant predictors of readiness. Results: Overall, 1,053 respondents from 22 universities in Sumatra were included in this study. Nearly 74.7% lacked formal AI training, and 90.9% relied on general tools like ChatGPT. The overall AI readiness mean was 74.36 (±14.03). Students received the highest score in ethics (10.96±2.46) and ability (28.20±5.57), but the lowest score in cognition (24.48±5.92). Prior AI training was the primary predictor for overall readiness (OR=1.90; 95% CI: 1.44–2.61). Coding experience significantly boosted cognitive readiness (OR=1.84; 95% CI: 1.33–2.54), while public university affiliation was strongly associated with higher vision (OR=2.20; 95% CI: 1.70–2.86) and ethical readiness (OR=2.10; 95% CI: 1.63–2.72). Conclusion: Medical students in Sumatra revealed moderate-to-high readiness, particularly in ethics and technical interest, yet lacked foundational cognitive proficiency. Structured curricula, hands-on practice, and early programming exposure are essential. Formal AI training is the key predictor to bridge this "literacy paradox" and ensure effective, ethical clinical integration.