Real Time AI and Machine Learning Systems for Privacy Preserving Digital Advertising in Healthcare
DOI:
https://doi.org/10.21590/ijtmh.09.02.06Keywords:
real-time AI, machine learning systems, privacy-preserving analytics, digital advertising, healthcare data protection, federated learning, differential privacy, secure data pipelines, intelligent targeting, ethical AI, cloud-native analytics, regulatory complianceAbstract
Real-time artificial intelligence and machine learning systems are increasingly shaping the future of digital advertising in healthcare, where personalization, regulatory compliance, and data privacy must be carefully balanced. This paper presents a privacy-preserving architectural framework for healthcare-focused digital advertising platforms that leverage real-time AI and machine learning while safeguarding sensitive patient and consumer data. The proposed system integrates distributed data processing, secure model training, and privacy-enhancing technologies to enable intelligent ad targeting, performance optimization, and contextual relevance without exposing personally identifiable or protected health information.
Machine learning models are deployed in real time to analyze behavioral signals, contextual metadata, and anonymized engagement patterns across healthcare digital channels. Privacy-preserving techniques such as data anonymization, tokenization, differential privacy, and federated learning are incorporated to ensure compliance with healthcare data protection regulations while maintaining model accuracy. The framework supports streaming analytics and low-latency inference to enable adaptive advertising strategies based on real-time user interactions, clinical content engagement, and platform performance metrics.
The architecture is designed for scalability and resilience, leveraging cloud-native principles, distributed machine learning pipelines, and automated orchestration to handle high-volume advertising workloads across healthcare ecosystems. Security-by-design principles are embedded throughout the system to protect data at rest, in transit, and during model execution. Continuous monitoring and AI-driven anomaly detection enhance trust by identifying misuse, bias, or policy violations in advertising workflows. By unifying real-time AI intelligence with privacy-preserving machine learning and secure data processing, the proposed approach enables ethical, compliant, and effective digital advertising in healthcare environments. This framework supports responsible innovation by aligning personalized advertising outcomes with patient trust, regulatory mandates, and the broader objectives of digital health transformation.
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