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Artificial Intelligence Integration in Radiology: Workforce Readiness and Implementation Challenges for Sustainable Healthcare Development

Publié
Serveur de preprints
Preprints.org
DOI
10.20944/preprints202606.1787.v1

Background: Artificial intelligence (AI) is increasingly transforming radiology by improving diagnostic efficiency, optimizing workflows, and enhancing healthcare sustainability. However, successful implementation depends on workforce readiness, organizational capacity, and effective governance. This study evaluated radiology professionals' perceptions of AI integration in Saudi Arabia in the context of sustainable healthcare development. Methods: A national cross-sectional survey was conducted using a validated 30-item questionnaire covering knowledge, attitudes, implementation readiness, and perceived barriers. Responses from 295 healthcare professionals were analyzed using descriptive statistics, Cronbach's alpha, exploratory factor analysis, Spearman correlation, and Kruskal–Wallis testing. Results: Participants demonstrated positive attitudes toward AI integration (3.57 ± 0.69) and moderate knowledge (3.35 ± 0.70), whereas implementation readiness was comparatively lower (3.04 ± 0.79). Perceived barriers showed the highest domain score (3.67 ± 0.64). Major barriers included implementation costs (3.98 ± 0.71), limited digital infrastructure (3.90 ± 0.75), insufficient staff training (3.84 ± 0.77), and lack of technical expertise (3.82 ± 0.78). Exploratory factor analysis identified four latent implementation dimensions: governance and trust, workforce capability, infrastructure integration, and organizational readiness. Positive correlations were observed between knowledge, attitudes, and implementation readiness. Conclusions: Radiology professionals support AI integration, but sustainable implementation is constrained by organizational, infrastructural, and governance-related barriers. Strategic investment in workforce development, digital infrastructure, governance frameworks, and cybersecurity is essential for responsible AI adoption and sustainable healthcare transformation.

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