Self-Reported Medical Artificial Intelligence Readiness in Residents: Attitudes, Anxiety, and Interprofessional Identity

Objectives: Resident physicians increasingly encounter artificial intelligence (AI), but perceived readiness may reflect attitudes and other self-assessments as well as exposure to AI.This exploratory study examined self-reported medical AI readiness in relation to AI attitudes, AI anxiety, interprofessional identity, and prior AI-or health-technology-related training. Materials and Methods:In this single-center cross-sectional online survey, all 136 residents in psychiatry, child and adolescent psychiatry, pediatrics, and family medicine at a university-affiliated training hospital in Türkiye were invited; 84 participated (61.8%).Associations among scale totals were examined using Spearman correlations with bootstrapped 95% confidence intervals (CIs) and Benjamini-Hochberg correction.A theory-informed post-hoc regression model and sensitivity analyses were used to examine independent associations with readiness.Results: Mean scores were 64.77 [standard deviation (SD) 16.40] for medical AI readiness, 112.74 (SD 17.67) for AI attitudes, 45.90 (SD 8.71) for interprofessional identity, and 70.43 (SD 27.63) for AI anxiety.AI attitudes correlated with readiness (ρ=0.32,95% CI 0.11-0.50,q=0.018), interprofessional identity (ρ=0.26,q=0.038), and AI anxiety (ρ=-0.29,q=0.020).In the regression model, more positive attitudes were associated with higher readiness (b=0.33,standardized β=0.36, 95% CI 0.11-0.55,p=0.004); estimates for anxiety, interprofessional identity, and prior training were imprecise.The attitude coefficient remained positive in sensitivity analyses examining influential observations, robust standard errors, and adjustment for demographic characteristics and specialty.Conclusion: More positive AI attitudes were associated with higher self-reported medical AI readiness in this single-center sample.Partial construct overlap and common-method variance may contribute to this association.The findings do not establish objective competence or causality; prospective studies using observed performance should evaluate clinically grounded AI education.