AI-driven aging digital twins: A roadmap for clinical translation in precision geriatrics
Clinical Snapshot
PICO Framework
| P — Population | Older adults and aging populations requiring precision geriatric care |
| I — Intervention | AI-driven Aging Digital Twin (ADT) technology for health monitoring and personalized interventions |
| C — Comparator | Traditional geriatric care approaches and diagnostic methods |
| O — Outcomes | Health monitoring effectiveness, predictive analytics accuracy, personalized intervention outcomes, early disease detection rates |
Bottom Line
This narrative review presents a conceptual roadmap for implementing AI-driven digital twins in geriatric care, promising personalized health monitoring and predictive analytics for older adults. While the technology shows theoretical potential for improving early disease detection and treatment optimization, significant barriers remain unresolved. Key concerns include age-related biases in training datasets, data quality variations in older populations, privacy protection, and lack of clinical validation studies. The review acknowledges these limitations but provides limited empirical evidence for clinical effectiveness. For Australian clinicians, this represents an emerging technology that may eventually complement traditional geriatric assessment tools, but current evidence is insufficient to support clinical implementation. Healthcare systems should monitor developments while prioritizing proven interventions for aging populations until robust validation studies demonstrate clinical utility and cost-effectiveness.
Key Findings
Effect Size: Not applicable - narrative review
Primary Outcome: Conceptual framework for AI-driven digital twins in geriatrics with identified implementation challenges
Nnt Or Sensitivity: Not applicable - no empirical data presented
Confidence Interval: Not provided
Clinical Application
Currently limited by technological maturity, regulatory frameworks, and implementation costs Potential alignment with Australia's Digital Health Strategy and My Health Record system, but would require significant TGA regulatory consideration for clinical implementation and PBS evaluation for cost-effectiveness Older adults in healthcare systems with advanced digital infrastructure and data integration capabilities
Abstract
Aging and related diseases account for ⁓70 % of global mortality with declining physiological homeostasis and cognitive function increasing susceptibility to neurodegenerative disorders, cardiovascular diseases and cancer. Treatment is hampered by imprecise diagnostics and limited therapeutic options. Precision medicine involving artificial intelligence (AI)-powered Aging Digital Twin (ADT) technology which integrates multi-source heterogeneous data via advanced AI and deep learning has great potential. Virtual doppelgangers enable real-time health monitoring, predictive analytics and personalized interventions to facilitate early disease detection and optimize risk assessment and treatment strategies. Challenges remain and include age-stratified biases in training datasets and variations in data quality for older populations which may limit model generalizability. Critical concerns regarding data privacy, algorithmic transparency and clinical validation of ADT models remain unresolved. The current review examines the transformative potential of AI-enabled ADTs in precision medicine for aging and highlights technical and ethical limitations.
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