Personalized risk reduction of hiv plans with artificial intelligence: a narrative review

Abstract

This narrative review explores the current landscape and future potential of utilizing Artificial Intelligence (AI) in the development and implementation of personalized risk reduction plans for individuals at risk of HIV infection. Traditional HIV prevention strategies often adopt a generic approach, overlooking the diverse and dynamic factors contributing to an individual's risk profile. In contrast, this review synthesizes existing literature to highlight recent advancements in AI applications, focusing on their role in tailoring HIV risk reduction interventions to the unique characteristics and circumstances of each individual. The review encompasses studies employing machine learning algorithms, predictive modeling, and data analytics to analyze and interpret large datasets related to HIV epidemiology, behavioral patterns, and socio-economic determinants. By providing an overview of these AI-driven methodologies, the review aims to showcase the potential for personalized risk assessment and intervention planning. Furthermore, it examines the integration of AI into mobile health applications, wearable devices, and telehealth platforms, facilitating real-time monitoring, feedback, and support for individuals seeking personalized risk reduction strategies.