Assessing pediatricians' readiness for artificial intelligence: a cross-sectional study in Istanbul, Türki̇ye.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare, including pediatrics, offering new opportunities for diagnosis, management, and decision support. However, the effective implementation of AI depends largely on healthcare professionals' knowledge, attitudes, and readiness to adopt these technologies. This study aimed to evaluate pediatricians' general attitudes toward artificial intelligence and their level of readiness for medical AI in Istanbul, Türkiye at tertiary hospital. Distinct from previous research focusing primarily on medical students, this study investigates the AI readiness of practicing pediatricians within a major tertiary hospital in Istanbul, addressing a critical gap in professional clinical workforce assessment. METHODS: This descriptive cross-sectional study was conducted between August 1 and November 1, 2025, at Prof. Dr. Cemil Taşcıoğlu City Hospital. A total of 130 pediatricians participated. Data were collected using a sociodemographic questionnaire, the GAAIS (General Attitudes toward Artificial Intelligence Scale), and the MAIRS-MS (Medical Artificial Intelligence Readiness Scale for Medical Students). The validity and reliability of scales in Turkish have been previously investigated in various studies. The internal consistency of the scales was excellent, with a Cronbach's alpha coefficient of 0.972 in our study. Statistical analyses were performed using SPSS 27. Correlation, t-test, ANOVA, and non-parametric tests were applied where appropriate. RESULTS: The mean age of participants was 37.47 ± 9.31 years, and 58.5% were female. The mean AI positive attitude score was 3.81 ± 0.74, and the negative attitude score was 2.83 ± 0.81. The total MAIRS-MS score was 74.97 ± 13.01, indicating a moderate-to-high level of AI readiness. Age was negatively correlated with AI positive attitude and total MAIRS score (p < 0.05). Participants with ≥ 21 years of professional experience had significantly lower AI readiness and positive attitude scores than younger participants (p < 0.05). A strong positive correlation was found between AI positive attitude and total MAIRS-MS score (r = 0.764, p < 0.01). Multiple regression analysis revealed that "Ability" (β = 0.516, p < 0.001) and "Cognition" (β = 0.323, p < 0.001) factors were the strongest predictors of a positive attitude toward AI (R2 = 0.658). A strong positive correlation was found between AI positive attitude and total MAIRS-MS score (r = 0.764, p < 0.01). These results suggest that clinical AI implementation must be accompanied by tailored institutional support and age-specific training to bridge the generational digital divide in pediatric care. CONCLUSION: Pediatricians generally demonstrated positive attitudes and moderate-to-high readiness for artificial intelligence in our study. However, advanced age and longer professional experience were associated with lower readiness levels. These findings highlight the need for a structured AI education and training program, especially targeting senior physicians, to ensure effective and ethical integration of AI into pediatric practice.