Review on AI-Driven Interactive Data Visualization and Predictive Analytics for Enhanced Decision Support in Health and Disease Management

Advancements in artificial intelligence (AI), data visualization, and predictive analytics have revolutionized de- cision support in healthcare, particularly in population health and disease management. This paper presents a novel frame- work integrating AI-driven interactive data visualization with predictive analytics to enhance decision-making processes in real- time. The system aims to address the complexities of healthcare data by leveraging electronic health records (EHRs), wearable devices, and population health data to improve disease pre- diction, outbreak detection, and resource allocation. A mixed- methods approach is employed to evaluate the effectiveness of AI-enhanced models, with quantitative analyses assessing the predictive accuracy and qualitative insights drawn from health- care practitioners. The results show a 25 percent improvement in disease prediction accuracy and a 30 percent reduction in decision-making time. However, challenges such as data privacy, algorithmic bias, and AI adoption barriers remain. The research also underscores the importance of ethical considerations and scalability in deploying AI-driven systems in diverse healthcare settings. Ultimately, the proposed framework offers a scalable, adaptable, and ethically sound solution for improving health outcomes and decision-making in population health management.