Precision Public Health: Harnessing AI and Big Data for Equitable Health Outcomes
DOI:
https://doi.org/10.65591/nt7xd590Keywords:
Population health, artificial intelligence, big data, precision public health, disease surveillance, risk prediction, machine learning, enhance equityAbstract
The integration of big data and artificial intelligence (AI) is transforming population health research by enabling more precise disease surveillance, risk prediction, and targeted interventions. By combining large-scale, heterogeneous datasets, including electronic health records, genomic profiles, wearable device outputs, environmental measures, and social determinants of health, AI systems can identify complex relationships, reveal hidden risk factors, and forecast health outcomes with unprecedented accuracy. These capabilities enhance early detection of epidemics, support individualized care planning, and inform policies aimed at reducing health disparities. Machine learning and deep learning techniques allow healthcare systems to manage resources more efficiently, predict service demand, and optimize allocation during both routine operations and public health emergencies. Applications extend to chronic disease prevention and management, precision public health initiatives, and policy simulations that model the potential impact of interventions such as vaccination strategies, environmental regulations, or taxation policies. Despite their promise, the integration of AI and big data into population health research presents significant challenges. These include safeguarding data privacy, ensuring cybersecurity, mitigating algorithmic bias, overcoming interoperability barriers, and addressing ethical concerns related to transparency and accountability. Effective use of these technologies requires interdisciplinary collaboration among data scientists, healthcare professionals, policymakers, and ethicists. This paper critically examines the roles, benefits, and limitations of AI and big data in advancing population health research. It highlights case studies demonstrating improved health outcomes and operational efficiencies, while also outlining frameworks for ethical governance and equitable implementation. By addressing current challenges, AI and big data hold the potential to revolutionize healthcare delivery, promote health equity, and enhance population-level well-being on a global scale.
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