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Digital infrastructures and data interoperability in radiology for prevention: from digital health to AI-enabled,
1Università degli Studi di Milano, Milan, Italy. io@lucasconfienza.it.
La Radiologia Medica
|July 13, 2026
Summary
Radiology is becoming data-centric, driven by AI and radiomics. Success requires robust interoperability and data governance, not just algorithms, to overcome fragmentation and ensure quality care.
Area of Science:
- Radiology and Medical Imaging
- Health Informatics
- Artificial Intelligence in Medicine
Background:
- Radiology is transitioning to a data-centric model supporting personalized medicine.
- Digital health, AI, and radiomics accelerate this evolution.
- Clinical impact hinges on data interoperability, not solely algorithms.
Purpose of the Study:
- To outline radiology-centric requirements for interoperable data ecosystems.
- To address challenges in implementing AI and radiomics in clinical workflows.
- To propose recommendations for scalable and secure data integration.
Main Methods:
- Focus on technical and semantic standards (DICOM, HL7 FHIR, IHE).
- Emphasis on data governance, quality programs, and AI integration.
- Discussion of practical barriers like legacy IT, semantic issues, and bias.
Main Results:
- Fragmented data, incomplete metadata, and inconsistent workflows hinder implementation.
- Insufficient governance compromises quality, privacy, and accountability.
- AI and radiomics integration faces challenges like model drift and medico-legal uncertainty.
Conclusions:
- Robust digital infrastructures and interoperability are crucial for AI and radiomics in radiology.
- Addressing data fragmentation, governance, and technical standards is essential.
- Pragmatic recommendations are needed for responsible deployment supporting prevention and personalized care.
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