Self-Healing Digital Twins: Hybrid Generative and Privacy-Preserving AI for Adaptive Wellness Platforms
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".