Self-Healing Digital Twins: Hybrid Generative and Privacy-Preserving AI for Adaptive Wellness Platforms
Revolutionizing Personalized Healthcare with AI-Powered Digital Twins
Artificial Intelligence (AI) has transformed personalized wellness platforms, yet challenges remain in adaptability, privacy, and user engagement. This paper introduces a Self-Healing Digital Twin Framework, integrating Hybrid Generative AI, Reinforcement Learning (RL)-based self-healing, and Privacy-Preserving AI (PPAI) to address these gaps. Unlike static Digital Twin models, our approach dynamically learns from multi-modal Internet of Medical Things (IoMT) and wearable data, ensuring real-time adaptation and privacy compliance via a dual-cloud architecture that eliminates raw data exposure. Validation through simulations and real-world deployment confirms the system's ability to track user health trends, detect anomalies, and optimize interventions for improved engagement and wellness outcomes. The RL-driven self-healing mechanism continuously refines recommendations, enhancing adherence. Our findings establish this framework as a scalable and privacy-secure AI-driven solution for intelligent, adaptive healthcare.
Authors: Nariman Mani, Salma Attaranasl
Executive Impact at a Glance
Key performance indicators demonstrating the transformative potential of our Self-Healing Digital Twin Framework in real-world healthcare applications.
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Adherence for Disengaged Users (RL-Based Healing)
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Engagement Increase
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Dropout Rate Reduction
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MAE for Sleep Prediction
Deep Analysis & Enterprise Applications
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AI-Driven Healthcare
This paper introduces a Self-Healing Digital Twin Framework that integrates Hybrid Generative AI, Reinforcement Learning (RL)-based self-healing, and Privacy-Preserving AI (PPAI) for personalized wellness management. It offers a secure, adaptive, and intelligent healthcare solution by dynamically learning from multi-modal IoMT and wearable data, ensuring real-time adaptation and privacy compliance through a dual-cloud architecture. The framework continuously refines recommendations, enhances user adherence, and proves effective in tracking health trends, detecting anomalies, and optimizing interventions.
Self-Healing Digital Twin Framework Process
The proposed dual-cloud architecture ensures secure, scalable, and real-time AI adaptation by decoupling data processing from AI-driven analysis, eliminating raw data exposure. The workflow includes data collection, anonymization, AI processing, and personalized recommendations.
Data Collection & Preprocessing (Cloud Env 1)
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Data Integration & Anonymization (Cloud Env 1)
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Anonymized Data Sent Securely
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Adaptive Digital Twin Engine (Cloud Env 2)
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AI-Based Anomaly Detection (Cloud Env 2)
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Reinforcement Learning-Based Healing (Cloud Env 2)
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Personalized Recommendations
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Feedback Learning for AI Improvement
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Real-Time Alerts / Status Updates
FL vs. Privacy-Preserving AI (PPAI) Benefits
A comparison highlighting the advantages of the proposed Privacy-Preserving AI (PPAI) framework over traditional Federated Learning (FL) in terms of privacy, scalability, and AI processing.
| FL Approach | Proposed PPAI Approach |
|---|---|
| Trains models on local devices. | Uses cloud-based AI with anonymized data. |
| Sends encrypted model updates. | Sends only de-identified feature vectors. |
| Requires high computing power on devices. | Works efficiently for all users. |
| Limits cloud AI processing. | Enables AI-driven Digital Twins securely. |
94%
Overall Anomaly Detection F1-Score
The framework achieves a robust 94% F1-score in anomaly detection, reliably flagging major health deviations while minimizing false positives.
AI-Driven Wellness/Lifestyle Management Platform Validation
The Self-Healing Digital Twin Framework was validated on an industry-grade AI wellness platform for goal-focused groups under certified health coaches. Users shared wearable and IoMT data, self-reported logs, and food images to receive personalized insights. AI-driven recommendations guided daily lifestyle adjustments, while social features boosted engagement. The Split-Process Privacy Framework, separating data collection/anonymization (Cloud Environment 1) from AI analysis (Cloud Environment 2), ensured privacy by removing personally identifiable information before processing. This architecture proved trustworthy and scalable, continuously improving recommendations, and personalizing in real-time.
Projected ROI: Self-Healing Digital Twins for Your Enterprise
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Projected Annual Savings
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Annual Hours Reclaimed
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Self-Healing Digital Twin Implementation Roadmap
A phased approach to integrating the Self-Healing Digital Twin Framework into your existing wellness or healthcare infrastructure, ensuring a smooth transition and optimal performance.
Data Integration & Anonymization Setup (Weeks 1-4)
Establish secure APIs for multi-modal data ingestion from IoMT/wearables. Implement PII removal and feature vector transformation in Cloud Environment 1.
Adaptive Digital Twin Engine Deployment (Weeks 5-8)
Deploy the Hybrid Generative AI models in Cloud Environment 2 to create personalized digital twins. Configure initial behavioral pattern tracking and anomaly detection algorithms.
Reinforcement Learning & Self-Healing Integration (Weeks 9-12)
Integrate RL agents for dynamic intervention optimization. Configure feedback loops for continuous learning and recommendation refinement.
Pilot Program & User Engagement (Weeks 13-16)
Launch pilot with a segment of users. Monitor engagement, adherence, and wellness outcomes. Gather initial user feedback for system fine-tuning.
Scaling & Continuous Optimization (Months 4+)
Expand deployment across the enterprise. Implement A/B testing for interventions and regularly update AI models based on evolving user data and health trends.
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