Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Privacy and security in teleradiology
0
Zitationen
1
Autoren
2010
Jahr
Abstract
Teleradiology is probably the most successful eHealth service available today. Its business model is based on the remote transmission of radiological images (e.g. X-ray and CT-images) over electronic networks, and on the interpretation of the transmitted images for diagnostic purpose. Two basic service models are commonly used teleradiology today. The most common approach is based on the message paradigm (off-line model), but more developed teleradiology systems are based on the interactive use of PACS/RIS systems. Modern teleradiology is also more and more cross-organisational or even cross-border service between service providers having different jurisdictions and security policies. This paper defines the requirements needed to make different teleradiology models trusted. Those requirements include a common security policy that covers all partners and entities, common security and privacy protection principles and requirements, controlled contracts between partners, and the use of security controls and tools that supporting the common security policy. The security and privacy protection of any teleradiology system must be planned in advance, and the necessary security and privacy enhancing tools should be selected (e.g. strong authentication, data encryption, non-repudiation services and audit-logs) based on the risk analysis and requirements set by the legislation. In any case the teleradiology system should fulfil ethical and regulatory requirements. Certification of the whole teleradiology service system including security and privacy is also proposed. In the future, teleradiology services will be an integrated part of pervasive eHealth. Security requirements for this environment including dynamic and context aware security services are also discussed in this paper.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.339 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.211 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.614 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.478 Zit.