<![CDATA[Artificial Intelligence (AI) has been increasingly adopted in radiology services to improve diagnostic and operational efficiency. However, its implementation requires substantial investment, highlighting the need for evaluation from both effectiveness and economic perspectives. This study aimed to evaluate the effectiveness of AI implementation in chest radiology services across Awal Bros Group Hospitals by assessing service turnaround time (TAT) and cost per case. A quantitative analytical study with a retrospective pre–post observational design was conducted using secondary data collected from eight hospitals within the Awal Bros Group network. Service performance before and after AI implementation was compared using turnaround time indicators, including waiting time and report interpretation time. An economic evaluation was also performed to assess operational efficiency and cost-effectiveness. The results demonstrated statistically significant improvements in service performance following AI implementation (p < 0.001). The median waiting time decreased from 12.08 to 9.65 minutes, while the mean report interpretation time decreased from 25.95 to 20.93 minutes. The economic evaluation further indicated improved operational efficiency and favorable cost-effectiveness, reflected by a lower cost per case after AI implementation.In conclusion, the implementation of AI in chest radiology services significantly improved operational efficiency by reducing turnaround time and optimizing service costs. These findings support the value of AI as a strategic digital health technology that enhances radiology workflow and contributes to the sustainability of hospital digital transformation.]]>
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