AI and Radiomics Depend on Data Infrastructure, Not Just Algorithms

Radiology is becoming a data-centric platform for prevention and personalized care, but the clinical impact of AI and radiomics depends on robust digital infrastructure and true interoperability across heterogeneous systems, not just better algorithms.

• Covers technical standards (DICOM, HL7 FHIR, IHE), semantic terminologies, and data governance required for interoperable imaging ecosystems.
• Discusses barriers: legacy IT, semantic inconsistencies, model drift, bias, medico-legal uncertainty, and hidden costs.
• Proposes pragmatic recommendations for scalable, secure, and clinically useful interoperability at institutional and network levels.

Perspective/review article; no original experimental data or systematic search methodology reported.

Automated summary

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