Authors : Hazel Galas Lampitoc; Reagan B. Ricafort

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/bdcvpe7a

Scribd : https://tinyurl.com/2s3jxfu9

DOI : https://doi.org/10.38124/ijisrt/26jun433

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapidly advancing biomedical imaging systems characterized by AI algorithms are transforming diagnostic processes, clinical decision making, and data handling. However, despite all of this progress, most healthcare environments struggle with fragmentation of imaging data, limited interoperability between PACS, VNA, and AI engines, and inefficient content routing across PACS, VNA, and AI engines. In this paper, we present a content networked framework to bring together the visual data, maximize content streams in the metadata for AI enhanced biomedical imaging systems by unifying and optimizing mapping, linking content between content and services, to have the full processing information for metadata and integrate various AI driven visualization tools for data to the workflow process. The framework sets up a structured content networking layer to support improved data access, retrieval efficiency, and intelligent triage. A consensus among expert validation from radiology, biomedical engineering, and health informatics professionals validated five constructs — competency, readiness, capability, performance, and organizational support — and have Cronbach’s alpha values of ~0.79–0.88. The findings validate that a framework like this one is feasible, scalable, and aligned with digital health transformation priorities, particularly within high demand clinical environments. This work lays the groundwork for future AI enabled imaging ecosystems and informs the establishment of interoperable, efficient, and patient focused diagnostic workflows.

Keywords : AI Enhanced Imaging, Content Networking, Biomedical Imaging Systems, Interoperability, PACS/VNA Integration, Clinical Workflow Optimization, AI Driven Diagnostics, Digital Health Transformation.

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