AI-DRIVEN ENVIRONMENTAL HEALTH DISEASE MODELING: A REVIEW OF TECHNIQUES AND THEIR IMPACT ON PUBLIC HEALTH IN THE USA AND AFRICAN CONTEXTS

Abstract

This scholarly paper embarks on an exploratory journey into the realm of AI-driven environmental health disease modeling, with a keen focus on its implications in the diverse healthcare landscapes of the USA and Africa. The study's background delves into the historical evolution of disease modeling techniques, emphasizing the revolutionary role of AI in modern public health strategies. It meticulously examines the comparative effectiveness of AI models in these distinct regions, addressing the challenges and opportunities inherent in AI-driven health models. Aiming to unravel the multifaceted impact of AI in disease prediction and public health policy, the paper navigates through various thematic corridors. It critically analyzes the significance of data sources and quality, ethical considerations in AI health modeling, and the integration of AI models into public health policies. The scope of the paper encompasses a comprehensive review of AI's efficacy in predicting environmental diseases, its role in enhancing disease surveillance systems, and the geographic and socioeconomic variations affecting model accuracy. The main findings reveal that AI models, while effective in disease prediction and surveillance, encounter challenges related to data integrity and ethical complexities. The study concludes that the integration of AI in healthcare necessitates a balanced approach, advocating for policies that support the development of context-specific AI models and address ethical concerns. Recommendations include fostering interdisciplinary collaboration and continuous evaluation of AI models to align them with evolving healthcare needs and ethical standards. This paper serves as a beacon for understanding AI's transformative potential in environmental health disease modeling, offering insights that are crucial for shaping future public health strategies and interventions.
 Keywords: AI in Healthcare, Disease Modeling, Public Health Policy, Data Quality, Ethical Considerations, Geographic Variations.