Authors: Keya Phukan
Abstract: The integration of Artificial Intelligence (AI) in healthcare has revolutionized predictive analytics, offering unprecedented opportunities to anticipate disease patterns, optimize patient outcomes, and improve resource allocation. Predictive healthcare analytics harnesses AI algorithms, including machine learning and deep learning, to analyze large datasets comprising electronic health records, medical imaging, genomic data, and lifestyle information. By detecting subtle patterns often invisible to human interpretation, AI enables earlier identification of at-risk patients, supports personalized treatment planning, and facilitates population-level health management. Such approaches are transforming the healthcare landscape by shifting the focus from reactive treatment to proactive prevention, significantly reducing morbidity and mortality while improving quality of care. Beyond clinical benefits, AI-driven predictive analytics enhances operational efficiency, allowing hospitals to forecast patient admissions, optimize staffing, and manage supply chains. Despite its potential, challenges persist, such as ensuring data quality, addressing algorithmic biases, protecting patient privacy, and integrating AI into existing healthcare infrastructures. Additionally, ethical considerations around transparency, accountability, and fairness must be carefully managed to foster trust in AI-driven predictions. This article critically explores the role of AI in predictive healthcare analytics, highlighting its applications, technological foundations, benefits, limitations, and future prospects. By examining both clinical and operational dimensions, it underscores how AI can fundamentally reshape healthcare into a more intelligent, anticipatory, and patient-centered system while emphasizing the importance of responsible and ethical implementation.
DOI: https://doi.org/10.5281/zenodo.16981149
