Digital infrastructures and data interoperability in radiology for prevention: from digital health to AI-enabled, quantitative imaging workflows.

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Abstract

Radiology is rapidly evolving from a service that produces images into a data-centric clinical platform that supports prevention, early diagnosis, and personalized care. This shift is accelerated by the convergence of digital health, artificial intelligence (AI), and quantitative imaging approaches such as radiomics. However, the clinical impact of these innovations depends less on algorithms alone and more on robust digital infrastructures and true interoperability across radiological, clinical, and-when available-molecular data. In practice, many implementations fail because data remain fragmented across heterogeneous systems, metadata are incomplete, workflows are not harmonized, and governance frameworks are insufficient to ensure quality, privacy, and accountability. This paper focuses on the radiology-centric requirements for interoperable data ecosystems, covering technical and semantic standards (e.g., DICOM, HL7 FHIR, IHE profiles, controlled terminologies), data governance and quality programs, and AI integration into real-world radiology workflows. Common practical barriers, including legacy IT debt, semantic inconsistencies, model drift, bias, medico-legal uncertainty, and hidden operational costs, are discussed. Last, pragmatic recommendations to enable scalable, secure, and clinically useful interoperability are proposed-supporting prevention pathways and the responsible deployment of AI and radiomics at institutional and network levels.

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