ArticleBMC medical informatics and decision making2026
Full-scale indexing and semantic annotation of CT imaging: boosting FAIRness.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe integration of artificial intelligence into medicine has led to significant advances, particularly in diagnostics and treatment planning. However, the reliability of AI models is highly dependent on the quality of the training data, especially in medical imaging, where varying patient data and evolving medical knowledge pose a challenge to the accuracy and generalizability of given datasets.
resultsThe proposed approach focuses on the integration and enhancement of clinical computed tomography (CT) image series for better findability, accessibility, interoperability, and reusability. Through an automated indexing process, CT image series are semantically enhanced using the TotalSegmentator framework for segmentation and resulting SNOMED CT annotations. The metadata is standardized with HL7 FHIR resources to enable efficient data recognition and data exchange between research projects.
conclusionsThe study successfully integrates a robust process within the UKSH MeDIC, leading to the semantic enrichment of over 1.7 million CT image series and over 50 million SNOMED CT annotations. The standardized representation using HL7 FHIR resources improves discoverability and facilitates interoperability, providing a foundation for the FAIRness of medical imaging data. However, developing automated annotation methods that can keep pace with growing clinical datasets remains a challenge to ensure continued progress in large-scale integration and indexing of medical imaging for advanced healthcare AI applications.
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