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The image is a digital infographic titled “AI & Data Privacy.” It visually explains how artificial intelligence processes data and how privacy is protected. At the enter, a neural network diagram represents the AI model. Arrows show how user input flows into the model, is processed mathematically, and produces AI output. The numbers displayed in the input and output boxes symbolize tokenized data being handled by the system. On the right side, stacked database icons represent data and metadata storage. Arrows connect these elements to concepts such as databases, access controls, usage limits, and anonymization, illustrating how information may be managed within an infrastructure. On the left, a friendly robot holding a shield symbolizes security and protection mechanisms. The shield represents safeguards designed to protect user information. At the bottom, the infographic highlights three core privacy principles:
Security, Data Management, and Transparency. Overall, the image communicates that AI systems process user input through mathematical models while privacy is protected through structured controls, data governance, and technical safeguards.
Databases are the structural basis of AI training. They provide curated, versioned and governed data that determine model quality, robustness and reproducibility. Data integrity and diversity directly influence performance and bias control. Data sovereignty defines legal control, storage location and access governance of datasets. It ensures regulatory compliance, protection of intellectual property and institutional autonomy, especially in distributed cloud environments. RAG databases connect AI models to external knowledge repositories. By retrieving relevant information at query time, they improve factual accuracy, reduce hallucinations and enable domain specific adaptation without retraining. Cybersecurity protects training data, embeddings and retrieval systems against data poisoning, leakage and unauthorized access. Secure architectures, encryption and strict access control are essential for resilient AI infrastructures.
Unique Device Identification-Based Linkage of Hierarchically Accessible Data Domains in Prospective Surgical Hospital Data Ecosystems: User-Centered Design Approach
Background: The electronic health record (EHR) targets systematized collection of patient-specific, electronically stored health data. The EHR is an evolving concept driven by ongoing developments and open or unclear legal issues concerning medical technologies, cross-domain data integration, and unclear access roles. Consequently, an interdisciplinary discourse based on representative pilot scenarios is required to connect previously unconnected domains.
Objective: We address cross-domain data integration including access control using the specific example of a unique device identification (UDI)-expanded hip implant. In fact, the integration of technical focus data into the hospital information system (HIS) is considered based on surgically relevant information. Moreover, the acquisition of social focus data based on mobile health (mHealth) is addressed, covering data integration and networking with therapeutic intervention and acute diagnostics data.
Methods: In addition to the additive manufacturing of a hip implant with the integration of a UDI, we built a database that combines database technology and a wrapper layer known from extract, transform, load systems and brings it into a SQL database, WEB application programming interface (API) layer (back end), interface layer (rest API), and front end. It also provides semantic integration through connection mechanisms between data elements.
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