The Role of Machine Learning in Healthcare: Predictive Models, Digital Interventions, and Randomized Clinical Trials

Abstract

Machine learning (ML) is transforming healthcare by providing advanced predictive models that help diagnose disease, optimize treatment, and monitor patients. These technologies enable clinicians to make more accurate, data-driven decisions, improve early detection of diseases, optimize personalized treatment plans, and continuously monitor patient health. This review explores the application of various ML models, including Random Forest, Support Vector Machines (SVM), and neural networks, which have shown considerable potential in analyzing large and complex medical data sets to predict patient outcomes and assist in clinical decision making. In addition, the review examines the current landscape of randomized clinical trials (RCTS) that assess the effectiveness of ML interventions in real care Settings. Despite the increasing implementation of ML in healthcare, there remains a significant need for rigorous RCTS to validate the clinical utility and safety of these models. In addition, the review discusses the integration of ML in digital health interventions, such as portable devices and mobile health applications, which are widely used for remote monitoring and chronic disease management. The study presents the potential of ML in healthcare while addressing ethical considerations, including transparency, algorithm accountability and data privacy. The importance of human loop systems, where health professionals work alongside ML models, is also highlighted as a key factor in improving the accuracy, reliability and adoption of these technologies in clinical practice.

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