Evaluating the Effectiveness of Cloud-Based AI and ML Techniques for Personalized Healthcare and Remote Patient Monitoring.
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Abstract
This comprehensive research paper evaluates the effectiveness of cloud-based artificial intelligence (AI) and machine learning (ML) techniques in personalized healthcare and remote patient monitoring. The study analyses various applications, including predictive analytics, natural language processing, computer vision, and wearable device integration. It examines the impact of these technologies on treatment plan optimization, drug discovery, risk stratification, and patient engagement. The research also investigates remote patient monitoring systems, focusing on real-time data analysis, anomaly detection, telemedicine integration, and chronic disease management. Through a rigorous evaluation framework, the study assesses clinical outcomes, cost-effectiveness, patient satisfaction, and healthcare provider feedback. Case studies in cardiovascular disease, diabetes, mental health, and post-operative care provide practical insights. The paper concludes by addressing challenges, limitations, and future directions for cloud-based AI and ML in healthcare, offering valuable recommendations for researchers, practitioners, and policymakers.