Population Health in Neurology and the Transformative Promise of Artificial Intelligence and Large Language Models.

Abstract

This manuscript examines the expanding role of population health strategies in neurology, emphasizing systemic approaches that address neurological health at a community-wide level. Key themes include interdisciplinary training in public health, policy reform, biomedical informatics, and the transformative potential of artificial intelligence (AI) and large language models (LLMs). In doing so, neurologists increasingly adopt a holistic perspective that targets the social determinants of health, integrates advanced data analytics, and fosters cross-sector collaborations-ensuring that prevention and early intervention are central to their efforts. Innovative applications, such as predictive analytics for identifying high-risk populations, digital twin technologies for simulating patient outcomes, and AI-enhanced diagnostic tools, illustrate the transition in neurology from reactive care to proactive, data-driven interventions. Examples of transformative practices include leveraging wearable health technologies, telemedicine, and mobile clinics to improve early detection and management of neurological conditions, particularly in underserved populations. These emerging methodologies expand access to care while offering nuanced insights into disease progression and community-specific risk factors. The manuscript emphasizes health disparities and ethical considerations in designing inclusive, data-driven interventions. By harnessing emerging technologies within frameworks that prioritize equity, neurologists can reduce the burden of neurological diseases, improve health outcomes, and establish a sustainable, patient-centered model of care benefiting both individuals and entire communities. This integration of technology, interdisciplinary expertise, and community engagement fosters a future where brain health is preventive, accessible, and equitable.