Implementation of artificial intelligence in the clinical management of longevity

Abstract

Artificial intelligence (AI) has become a central driver in healthy longevity medicine (HLM), offering new tools to characterize biological aging trajectories, identify preclinical physiological decline, and optimize interventions aimed at preserving function throughout the lifespan by targeting age-related processes. HLM is increasingly recognized as a specialty focusing on the multidimensional process of aging, encompassing molecular, physiological, cognitive, and behavioral components, all of which generate complex, high-dimensional datasets that exceed the analytical capacity of traditional clinical approaches. AI methodologies, including machine learning and deep learning models capable of integrating large, multimodal data streams, provide the computational infrastructure required to produce actionable insights. In the clinical practice of HLM, AI further facilitates integration of converging domains, including continuous digital phenotyping enabled by wearables and sensors, advanced biomarker modeling, predictive modeling capable of forecasting risk trajectories and personalized intervention optimization through life models and digital twins. These models support anticipatory clinical management, shifting care from reactive disease treatment toward continuous preservation of physiological resilience. Despite rapid progress, the integration of AI into routine healthy longevity care requires careful consideration of data quality, algorithmic transparency, regulatory frameworks, population diversity, and clinical interpretability. Nonetheless, AI-driven healthy longevity management is beginning to allow biological aging to be quantified, targeted, and longitudinally monitored in clinical practice.

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