Personalized Healthcare Through AI and Shaping the Future of Diagnostics and Therapy

Abstract

Artificial Intelligence (AI) integration into healthcare drives advances in diagnostics, personalized medicine, and patient care. This project implements AI and machine learning to improve early disease detection, risk prediction, and individualized therapies by analyzing electronic health records, genetic profiles, and wearable device data. The system proposes AI-powered predictive models that enhance diagnostic precision, personalize treatments, and optimize healthcare delivery processes. AI models contribute to real-time clinical decision-making, resource allocation, and remote patient monitoring through integration with cloud platforms and IoT-enabled devices. This approach reduces diagnostic errors, improves patient engagement, and lowers healthcare costs by enabling early interventions and personalized care strategies. Despite challenges related to data privacy, algorithm bias, and ethical concerns, interdisciplinary collaboration supports responsible AI adoption. Reported studies demonstrate how AI-driven personalized healthcare systems deliver scalable, efficient, and patient-centered outcomes, reshaping the future of medical practice.

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