Secure and Intelligent Healthcare: Applications of Machine Learning and Data Analytics

Abstract

The high-speed digitalization of the medical field has brought the incorporation of the latest computational technologies that should enhance clinical quality, efficiency, and system safety. The given review examines the ways in which machine learning and data analytics may be utilized to create intelligent healthcare systems that can be used to facilitate predictive diagnostics, clinical decision-making that will be and resource management that will be optimized. It demonstrates that artificial intelligence helps to improve the accuracy of diagnosis and the efficiency of business processes and that smart supply chain models improve inventory prediction and distribution resilience. The article compares the application of AI in precision agriculture, but focuses more on overall implications of predictive analytics and data-driven logistics. Moreover, the paper explains the rising significance of cybersecurity systems to the safety of sensitive patient information, and the integrity of systems in progressively interconnected digital ecosystems. The issues of regulations, ethics and implementation are discussed to highlight the necessity of transparent, secure and compliant technology adoption. On the whole, the review shows that machine learning, data analytics, artificial intelligence, secure supply chains, and robust cybersecurity coming together will be the basis of ensuring resilient, patient-centered, and future-ready healthcare ecosystems.