The integration of Artificial Intelligence (AI) into predictive healthcare analytics is revolutionizing the landscape of modern medicine. By leveraging large-scale health data from sources such as electronic health records (EHRs), wearable devices, and unstructured clinical notes, AI facilitates early disease detection, personalized treatments, and improved patient management. This paper examines the fundamentals of predictive healthcare analytics, the role of AI-driven modeling techniques, including machine learning and natural language processing, and the diverse data sources fueling this transformation. Furthermore, it examines the types of predictive models, implementation challenges, and case studies illustrating real-world applications in telemedicine and resource-constrained settings. Ethical considerations surrounding data use, model transparency, and patient safety are also critically analyzed. The study concludes by identifying emerging trends and forecasting the continued expansion of AI in predictive healthcare systems, offering stakeholders a framework to responsibly harness these technologies for optimized care delivery and population health management. Keywords: Artificial Intelligence, Predictive Analytics, Healthcare, Machine Learning, Electronic Health Records (EHR), Natural Language Processing (NLP), Risk Prediction.
Nagawa Jackline Irene (Sun,) studied this question.