Artificial Intelligence and Machine Learning for Smart Healthcare: A Comprehensive Review
Abstract
The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) is transforming healthcare by enabling intelligent diagnosis, personalized treatment, clinical decision support, and continuous patient monitoring. This review provides a comprehensive analysis of the application of AI and ML techniques in modern healthcare systems, with particular emphasis on medical image analysis, disease prediction, electronic health records, drug discovery, and personalized medicine. Various machine learning approaches, including supervised, unsupervised, deep learning, and reinforcement learning, are examined in terms of their effectiveness and applicability to healthcare challenges. The integration of AI with Internet of Things (IoT)-enabled medical devices is also explored, highlighting opportunities for real-time monitoring and remote healthcare delivery. Furthermore, the review discusses major challenges related to data privacy, model interpretability, bias, cybersecurity, interoperability, and regulatory compliance. Recent developments in explainable AI, federated learning, and edge intelligence are considered as promising solutions to address these challenges. The review concludes by identifying emerging research directions and emphasizing the importance of trustworthy, secure, and human-centered AI for the development of next-generation smart healthcare systems.
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