PRECISION HEALTH REIMAGINED: GENAI, LLMS & PREVENTIVE CARE AT POPULATION SCALE

Advancements in Generative Artificial Intelligence and Large Language Models are redefining precision health by enabling population-scale disease prediction, early intervention, and personalized prevention strategies. These technologies enhance the ability of health systems to analyze large, diverse datasets including genomic information, electronic health records, lifestyle patterns, and environmental exposures to identify risk indicators and forecast disease trajectories. GenAI models assist clinicians and public health professionals by generating individualized prevention pathways, simulating disease outcomes, and supporting adaptive health communication. LLMs strengthen this ecosystem by processing unstructured medical text, extracting risk factors, interpreting clinical guidelines, and supporting decision-making across communities. At the population level, AI-driven models enable targeted screening, behavioural risk modelling, and resource optimization, contributing to more proactive and equitable healthcare systems. Despite these advancements, integrating GenAI and LLMs into precision health poses challenges linked to data quality, privacy, model bias, clinical interpretability, and governance. This chapter synthesizes emerging research to explore how GenAI and LLMs can transform preventive healthcare, enhance early interventions, and support scalable, personalized public health strategies. It concludes by outlining practical implications, ethical considerations, and future research opportunities to ensure responsible and effective deployment of AI-enabled precision health systems.