Evaluation of healthcare workers' readiness for the use of artificial intelligence in healthcare in Uşak province

Aim: Artificial intelligence (AI) technologies are increasingly being integrated into healthcare services, and their effective and safe implementation is closely related to healthcare workers' readiness levels. This study aimed to evaluate the readiness of healthcare workers in Uşak province for medical artificial intelligence systems across cognitive, skill-based, predictive, and ethical dimensions, and to identify factors associated with readiness. Materials and Methods: This descriptive, cross-sectional study was conducted with 375 healthcare workers employed in public and private healthcare institutions in Uşak province. Data were collected through face-to-face interviews using a questionnaire assessing sociodemographic characteristics and perceptions, knowledge, and attitudes toward AI, along with the Medical Artificial Intelligence Readiness Scale (MAIRS-MS). Descriptive statistics, Student's t-test, and one-way ANOVA were used for data analysis. Statistical analyses were performed using SPSS software, and a p value <0.05 was considered statistically significant. Results: The majority of participants had not received formal training on artificial intelligence in their professional practice. Higher readiness levels across all subdimensions and total scale scores were observed among participants with greater AI-related knowledge, higher trust in AI applications, positive perceptions regarding AI's contribution to professional development, and willingness to receive AI-related training (p<0.001). Male participants demonstrated significantly higher cognitive, skill, and predictive scores as well as total readiness scores compared to female participants, while no gender-based difference was found in the ethical dimension. Physicians exhibited significantly higher readiness levels than other healthcare professional groups. Additionally, increasing age and longer professional experience were associated with a decline, particularly in skill and predictive dimension scores. Conclusions: This study demonstrates that healthcare workers' readiness for medical artificial intelligence is a multidimensional construct influenced by knowledge, attitudes, education, professional role, and level of technological engagement. The findings highlight the need for targeted educational interventions and institutional strategies to support the effective integration of AI technologies into healthcare services. Key Words: Artificial Intelligence, Healthcare Workers, Digital Health, Health Services, Medical Artificial Intelligence Readiness Scale