Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
DICOM: The Future of Healthcare Interoperability?
0
Zitationen
1
Autoren
2026
Jahr
Abstract
Abstract Healthcare interoperability has traditionally relied on structured standards such as HL7 and Fast Healthcare Interoperability Resources (FHIR), which remain complex and costly to implement and maintain. This article evaluates the potential of Digital Imaging and Communications in Medicine (DICOM), combined with artificial intelligence (AI), as an alternative paradigm for data exchange. Drawing on current literature and real-world implementations, it highlights how DICOM enables standardized, high-fidelity data sharing, while advances in generative and multimodal AI allow extraction of structured clinical insights directly from images. Emerging applications in radiology and pathology demonstrate reduced integration complexity and improved data fidelity, although this approach introduces greater data storage and computational demands. DICOM augmented by AI represents a promising complementary model to traditional interoperability standards; however, HL7 and FHIR are likely to remain relevant within hybrid frameworks as technical, regulatory, and clinical adoption challenges evolve.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.508 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.393 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.864 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.564 Zit.