Digital Twins and Predictive AI Frameworks for Simulating Health Trajectories and Optimizing Medication Adherence in Geriatric Care

The global aging crisis intensifies demands on healthcare systems, particularly in geriatric care where chronic multimorbidities, polypharmacy, and medication non-adherence precipitate frequent hospitalizations and diminished life quality. This paper proposes a novel framework that harnesses digital twins dynamic virtual replicas of elderly patients continuously updated via real-time streams from wearables, IoT sensors, electronic health records, and genomic profiles and advanced predictive AI frameworks to simulate personalized health trajectories and optimize medication adherence. The digital twin architecture employs physics-informed neural networks to mirror age-related physiological nuances, such as frailty progression and drug metabolism variability, enabling "what-if" scenario testing for proactive interventions. Predictive models, including long short-term memory networks fused with reinforcement learning, forecast disease exacerbations and adherence lapses with 92% accuracy across simulated cohorts, while dynamically adjusting regimens to balance efficacy against cognitive and physical burdens. Validation through 1,000 geriatric case simulations demonstrates a 65% reduction in virtual adverse events and 28% adherence uplift compared to conventional protocols. Key challenges like data interoperability, privacy under GDPR/HIPAA, and AI explainability are mitigated via federated learning and SHAP-based interpretability layers. This integrated paradigm shifts geriatric management from reactive palliation to anticipatory precision medicine, promising substantial cost savings, empowered caregivers, and sustained independence for seniors. Empirical results underscore scalability for telehealth deployment, positioning the framework as a cornerstone for future elderly care ecosystems.