Assessing the Efficiency Gains of AI-integrated Pediatric Radiology's Picture Archiving and Communication Systems in Pediatric Radiology Departments

Abstract

The integration of Artificial Intelligence (AI) into Picture Archiving and Communication System (PACS) has emerged as a pivotal advancement in pediatric radiology, with potential efficiency gains meriting evaluation. This study aimed to assess the impact of AI-integrated PACS on workflow, diagnosis, and image quality within pediatric radiology departments. Immediate benefits included accelerated preliminary diagnosis, with AI algorithms quickly scanning and prioritizing abnormal regions, reducing average read times. This rapid processing allowed radiologists to devote more attention to complex cases. Enhanced image clarity was observed, with AI optimizing parameters in real-time, resulting in a reduction of retakes and minimized radiation exposure. This is especially crucial in pediatrics, given the sensitivity of developing bodies to radiation. Critically, the AI's capability for severity-based triage became evident as it auto-prioritized life-threatening conditions, facilitating swift intervention. Furthermore, the AI-assisted workflow showed efficiency via automated report generation and integrated data collation, presenting a holistic patient view for radiologists. However, while such innovations promise to reshape pediatric radiology, they bring forth challenges. Data privacy concerns, especially around sensitive pediatric data, and the potential for over-reliance on AI algorithms underscore the need for rigorous oversight and protocol establishment. AI-integration into PACS in pediatric radiology presents substantial efficiency gains, optimizing diagnosis and care quality. Yet, its implementation requires a considered approach, balancing benefits with emergent challenges.