Authors : Kehinde Hassan

Volume/Issue : Volume 11 - 2026, Issue 6 - June

Google Scholar : https://tinyurl.com/mvab6y9k

Scribd : https://tinyurl.com/4w3vsm6z

DOI : https://doi.org/10.38124/ijisrt/26jun1450

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial Intelligence (AI) has emerged as a crucial driver of contemporary, effective, and patient-centred healthcare systems, due to the rapid digital transformation of the healthcare industry. Enterprise-scale analytics tools are becoming increasingly essential for promoting operational excellence and value-based care, as health organizations worldwide struggle with rising expenses, complex data, and the demand for quality improvement. This review explores the integration of AI technologies, including predictive analytics, machine learning, and automation, into healthcare systems. The major findings reveal that AI-driven platforms improve preventive care, enhance population health management, optimize resource utilization, reduce avoidable hospitalizations, and support cost containment. Additionally, these platforms strengthen evidence-based decision-making and collaboration among clinicians. Effective governance and transparent validation frameworks are critical to ensuring algorithmic accountability, ethical compliance, and long-term sustainability of AI-enabled health systems. AI-driven enterprise platforms are therefore essential for modernizing healthcare, enabling better patient outcomes, efficient operations, and a sustainable value-based care ecosystem.

Keywords : Artificial Intelligence, Healthcare Analytics, Machine Learning, Operational Efficiency.

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