Artificial Intelligence (AI) is transforming healthcare delivery through its applications in early disease detection and personalized treatment planning. This comprehensive technical article examines the current landscape of AI integration in medical practice, highlighting how advanced algorithms analyze complex healthcare data to identify disease indicators earlier than conventional methods and develop individualized therapeutic approaches. It covers supervised, unsupervised, and reinforcement learning techniques being applied across various medical domains, particularly in oncology and cardiovascular disease. By leveraging diverse data sources—including electronic health records, medical imaging, genomic information, and wearable device data—AI systems demonstrate promising capabilities in revolutionizing diagnostic accuracy, treatment selection, and chronic disease management. The article also addresses significant challenges in implementing healthcare AI, including data quality concerns, integration difficulties, regulatory uncertainties, and ethical considerations. As healthcare organizations navigate these implementation barriers, emerging approaches such as federated learning, explainable AI, and continuous learning systems offer potential solutions to expand AI adoption while ensuring equitable, transparent, and clinically valuable applications.
Keywords: artificial intelligence in healthcare, clinical implementation challenges, early disease detection, machine learning algorithms, personalized medicine
