Technology Leads in Community Needs: AI Applications in Preventive Health

Abstract

Artificial intelligence (AI) is driving a rapid shift in healthcare from reactive therapy to proactive prevention. Researchers investigate AI applications for enhancing community preventive health services through risk identification, customized treatments, and optimized resource management. The research evaluates AI applications in community-based preventive health programs through a systematic review of studies published between 2018 and 2025. The research evaluates AI performance against conventional methods through its analysis of disease prediction accuracy, risk evaluation, health equality, and patient participation results. The research followed PRISMA 2020 guidelines to conduct its systematic review. The search of Web of Science, IEEE Xplore, PubMed, and Scopus databases revealed studies from January 2018 to August 2025. The research team evaluated twelve studies that met the inclusion criteria through thematic analysis after performing a quality assessment using established validation methods. The concluded 12 studies from 2018 to 2025 demonstrate how AI applications in preventive health have evolved into four distinct areas: (1) AI-assisted screening and predictive models enhance risk assessment and early disease detection capabilities; (2) Health promotion and patient engagement receive personalized approaches; (3) The implementation of AI systems creates both positive effects on underserved areas and negative consequences through algorithmic bias and digital access disparities; (4) The process of implementation faces various obstacles. The implementation of artificial intelligence technology enables community health programs to achieve better results through enhanced precision, targeted approaches, and unbiased delivery. The path to success requires solving three main sets of challenges, which include algorithmic fairness, technological advancement, and social acceptance. The research presents a method to enhance public health systems through AI implementation, which requires special attention to Libyan and similar contexts.