Building Scalable and Automated Verification Tools for Healthcare Imaging Systems

Traditional verification of healthcare imaging systems remains burdened by manual effort, fragmented vendor implementations, and limited traceability. Ensuring compliance with DICOM conformance statements across interacting components often requires testers to repeat labour-intensive checks across multiple systems, configurations, and protocol variants. As standards evolve and legacy infrastructures are retired, these activities struggle to keep pace with modern release cycles and become increasingly unsustainable, error-prone, and difficult to scale. This paper presents a human-centered automation framework that modernizes healthcare imaging verification through a unified pipeline of automated tools spanning key software subsystems. The framework abstracts underlying tool complexity and enables engineers and QA professionals to collaboratively execute, monitor, and interpret verification tasks. Guided by human-factor design principles, the approach lowers cognitive overhead, streamlines reporting, and improves transparency through repeatable workflows and traceable verification artifacts. The result is a scalable, adaptive, and user-friendly verification process that bridges standards-driven compliance with modern software engineering practices.