From Lifestyle Medicine to Algorithmic Health: The Role of Artificial Intelligence in Redefining Preventive Wellness

Abstract

Non-communicable diseases now represent the leading cause of morbidity and mortality worldwide, exposing the shortcomings of healthcare systems designed primarily around acute and reactive care. This reality has sparked growing interest in preventive frameworks that can anticipate risk, personalize guidance, and sustain engagement over time. The present review critically examines how artificial intelligence is accelerating this shift, moving preventive wellness from the domain of generalized lifestyle advice toward what we term “algorithmic health”—a data-driven paradigm built on continuous monitoring, predictive analytics, and adaptive intervention.Drawing on a narrative synthesis of multidisciplinary literature spanning digital health, machine learning, behavioral science, and public health, the review traces the conceptual arc from traditional lifestyle medicine to AI-enabled prevention. It evaluates foundational AI technologies, the role of digital biomarkers and interconnected data ecosystems, strategies for personalized and behaviorally responsive interventions, and clinical as well as population-level applications.The evidence suggests that AI meaningfully enhances preventive care by supporting early disease detection, enabling real-time physiological monitoring, and delivering interventions that adapt to individual risk profiles and behavioral patterns. Wearable devices, intelligent recommender systems, and predictive models illustrate how precision wellness can operate at scale. At the same time, the review identifies persistent barriers—data fragmentation, infrastructural inequities, algorithmic bias, and unresolved questions around privacy and governance—that temper enthusiasm with caution.The review concludes that realizing the full potential of AI-driven prevention will require interoperable data architectures, inclusive and human-centered design, and regulatory frameworks that keep pace with technological change. Interdisciplinary collaboration and a commitment to equitable access remain indispensable if algorithmic health is to deliver on its promise across diverse populations and settings.