“Content Networking Framework for AI Enhanced Biomedical Imaging Systems”

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.

AI Enhanced Imaging, Content Networking, Biomedical Imaging Systems, Interoperability, PACS/VNA Integration

1. Abolmaesumi, P., & Fichtinger, G. (2020). Artificial intelligence and medical imaging: Opportunities and challenges. Springer. https://doi.org/10.1007/978-3-030-37188-3 (doi.org in Bing) [Google Scholar] [Crossref]

2. Arenson, R. L., Andriole, K. P., Avrin, D. E., & Gould, R. G. (2019). The digital imaging and communications in medicine (DICOM) standard: A review. Journal of Digital Imaging, 32(4), 620–633. https://doi.org/10.1007/s10278-019-00232-0 (doi.org in Bing) [Google Scholar] [Crossref]

3. Bui, A. A., & Taira, R. K. (2010). Medical imaging informatics. Springer. https://doi.org/10.1007/978-1-4419-0385-3 (doi.org in Bing) [Google Scholar] [Crossref]

4. Doi, K. (2007). Computer-aided diagnosis in medical imaging: Historical review, current status, and future potential. Computerized Medical Imaging and Graphics, 31(4–5), 198–211. https://doi.org/10.1016/j.compmedimag.2007.02.002 (doi.org in Bing) [Google Scholar] [Crossref]

5. Huang, H. K. (2019). PACS and imaging informatics: Basic principles and applications (3rd ed.). Wiley-Blackwell. (No DOI available) [Google Scholar] [Crossref]

6. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101 (doi.org in Bing) [Google Scholar] [Crossref]

7. Kahn, C. E., Carrino, J. A., Flynn, M. J., Peck, D. J., & Horii, S. C. (2019). Imaging informatics for healthcare professionals. Springer. https://doi.org/10.1007/978-3-030-13960-5 (doi.org in Bing) [Google Scholar] [Crossref]

8. Kumar, V., Abbas, A. K., & Aster, J. C. (2020). Robbins and Cotran pathologic basis of disease (10th ed.). Elsevier. (No DOI available) [Google Scholar] [Crossref]

9. Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., & Kern, C. (2019). A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271–e297. https://doi.org/10.1016/S2589-7500(19)30123-2 (doi.org in Bing) [Google Scholar] [Crossref]

10. Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019). Deep learning in radiology: An overview of the concepts and a survey of the state of the art. Radiology, 293(2), 350–367. https://doi.org/10.1148/radiol.2019191301 (doi.org in Bing) [Google Scholar] [Crossref]

11. O’Connor, S. D., & Andriole, K. P. (2021). Quality and reliability considerations in radiology AI research. Journal of the American College of Radiology, 18(9), 1280–1287. https://doi.org/10.1016/j.jacr.2021.05.007 (doi.org in Bing) [Google Scholar] [Crossref]

12. Rosenfeld, A., & Thurston, M. (1971). Edge and curve detection for visual scene analysis. IEEE Transactions on Computers, 20(5), 562–569. https://doi.org/10.1109/T-C.1971.223310 (doi.org in Bing) [Google Scholar] [Crossref]

13. Shortliffe, E. H., & Cimino, J. J. (2014). Biomedical informatics: Computer applications in health care and biomedicine (4th ed.). Springer. https://doi.org/10.1007/978-1-4471-4474-8 (doi.org in Bing) [Google Scholar] [Crossref]

14. Smith, K., & Tan, J. (2018). Workflow optimization in radiology: Principles and applications. Journal of Medical Systems, 42(3), 45. https://doi.org/10.1007/s10916-018-0901-5 (doi.org in Bing) [Google Scholar] [Crossref]

15. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. (No DOI available) [Google Scholar] [Crossref]

16. World Health Organization. (2021). Saudi Arabia: Health system transformation and digital health strategy. WHO Press. (No DOI available) [Google Scholar] [Crossref]

• Block-Based Programming for Education: A Comprehensive Analysis of Visual Programming Environments in K-12 Learning
• Management of Academic Libraries and Client Satisfaction Towards Digital Utilization: Basis for Monitoring Library Operations in SOCCSKSARGEN Region.
• Revenue Leakages in TPA Insurance Claims and Corporate Claims: An Institutional Overview of Aster Prime Hospital, Hyderabad
• Technology and Innovation in Hospitality and Tourism: A Management Perspective
• Geospatial Distribution of Tarok Sacred Grove of Langtang North and Langtang South Local Government Areas