AI-DRIVEN RISK STRATIFICATION IN HEALTHCARE: ADVANCING EARLY DETECTION AND PATIENT OUTCOME OPTIMIZATION - Abrar Ahmed Syed, (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2025)
This article examines the transformative role of Artificial Intelligence in healthcare systems, focusing on its application in risk stratification and patient outcome optimization. The article explores how AI technologies revolutionize healthcare delivery through advanced data analysis, predictive modeling, and clinical decision support systems. It shows the integration of diverse data sources, including electronic health records, genetic information, and real-time health metrics. It demonstrates how these comprehensive datasets enable more accurate patient risk assessment and early intervention strategies. The article analyzes AI systems' implementation challenges and successes in clinical settings, examining workflow integration, provider adoption, and system scalability. Furthermore, it evaluates AI implementation's clinical and financial impacts, highlighting improvements in patient outcomes, resource optimization, and cost-effectiveness. The article also addresses critical considerations regarding data privacy, ethical implications, and future directions for AI in healthcare, emphasizing the importance of balancing technological advancement with ethical responsibility and patient care quality.