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Lifestyle medicine (LM) offers a transformative, evidence-based approach to preventing, managing, and potentially reversing chronic diseases by targeting modifiable lifestyle factors such as nutrition, physical activity, sleep, stress, substance use, and social connectivity. However, real-world implementation of LM is often hindered by patient adherence issues, limited clinical time, and the need for ongoing personalized support. Artificial intelligence (AI), with its capabilities in data processing, pattern recognition, and predictive modeling, presents a unique opportunity to overcome these barriers and enhance the reach and precision of LM interventions. This narrative review explores AI's integration into LM's core domains. In nutrition, AI facilitates real-time dietary assessment and personalized recommendations through image recognition and machine learning. In physical activity and fitness, AI-powered wearable devices deliver tailored feedback, support virtual coaching, and predict injury risk. AI applications in sleep medicine allow for continuous, non-invasive monitoring and the early detection of sleep disorders. AI-driven cognitive behavioral therapy chatbots and biosensor-based stress prediction tools provide scalable, cost-effective support for mental health and stress management. Moreover, AI is pivotal in chronic disease prevention by integrating lifestyle data with electronic health records to forecast disease trajectories and optimize interventions. Despite these advances, several challenges remain. Data privacy concerns, algorithmic bias, regulatory ambiguities, and varying user trust and engagement levels must be addressed to ensure equitable and ethical implementation. AI's integration with digital twin technology and precision LM represents the next frontier in personalized health. As LM continues to evolve, AI will be indispensable in driving a more proactive, participatory, and person-centered model of care that meets the complex demands of chronic disease management in the 21st century.
Saeed et al. (Sun,) studied this question.