AI-Driven Transformation of Healthcare: From Predictive Analytics to Personalized Medicine
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The rapid evolution of artificial intelligence (AI) technologies has introduced a paradigm shift in modern healthcare, reshaping the continuum of care from early disease prediction to fully personalised therapeutic strategies. This paper provides a comprehensive examination of AI-driven healthcare transformation, encompassing predictive analytics, medical imaging diagnostics, genomic medicine, clinical decision support, and patient-specific treatment planning. By synthesising current literature, deploying a structured methodology, and presenting an in-depth case study, this study demonstrates that AI systems consistently outperform traditional clinical processes in speed, accuracy, and cost-efficiency. Specifically, AI-assisted diagnostics achieve accuracy rates of up to 94% compared to 68–74% for conventional approaches, while predictive models reduce unnecessary hospital readmissions by approximately 30%. A case study examining the deployment of an AI-based predictive analytics platform in a 500-bed tertiary hospital validates these findings with empirical outcome data. The paper also critically examines limitations including algorithmic bias, data privacy challenges, regulatory barriers, and the digital divide, and projects the future trajectory of AI healthcare through federated learning, explainable AI, and precision oncology. Twenty peer-reviewed references are cited throughout.
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