A Review Article: The Application of Artificial Intelligence in Population and Family Health Principles in Medicine

This review aims to synthesize current evidence on the application of AI technologies in the promotion, monitoring, and management of population and family health. The goal is to identify opportunities, challenges, and ethical considerations in deploying AI within this critical domain of public health and clinical practice. The findings reveal that AI applications in population and family health are expanding rapidly, particularly in disease prediction models, personalized health interventions, mobile health (mHealth) platforms, and automated screening tools. AI has shown promise in enhancing early detection of maternal complications, improving vaccination coverage through predictive analytics, and tailoring family planning services based on population needs. However, challenges such as data privacy, algorithmic bias, unequal access to AI technologies, and the need for transparent regulatory frameworks remain significant barriers to widespread implementation. AI holds significant potential to revolutionize population and family health by enabling proactive, data-driven, and equitable healthcare delivery. To realize its full benefits, collaborative efforts are needed across disciplines to ensure ethical integration, robust data governance, and culturally sensitive AI solutions. This review underscores the importance of investing in AI literacy, infrastructure, and policy frameworks to ensure that technological advances align with the core principles of public health and medicine.

Keywords:

Artificial Intelligence , Population Health , Family Health , Machine Learning , Predictive Analytics , Health Equity , Digital Health , Preventive Medicine