Using AI, ML, and Big Data in Contemporary Healthcare Systems to Provide Precision Patient Care

Abstract

The integration of Artificial Intelligence (AI), Machine Learning (ML), and Big Data analytics in healthcare systems represents a paradigm shift toward precision medicine and personalized patient care. This research paper examines the current applications, methodologies, and outcomes of these technologies in contemporary healthcare settings. Through systematic analysis of recent literature and evaluation of implementation frameworks, we demonstrate how AI-driven approaches enhance diagnostic accuracy, optimize treatment protocols, and improve patient outcomes. Our findings indicate that ML algorithms achieve diagnostic accuracies of up to 94.2% in specific clinical applications, while Big Data analytics reduces treatment response time by 35% on average. The study presents a comprehensive framework for implementing precision healthcare solutions and discusses challenges including data privacy, algorithmic bias, and system integration. Results suggest that successful implementation requires multidisciplinary collaboration, robust data governance, and continuous model validation to ensure clinical efficacy and patient safety.