Today, Machine Learning (ML) plays a transformative role in IoT-cloud enabled healthcare by enabling intelligent analysis of real-time patient data collected through wearable devices, sensors, and smart medical systems. ML models support disease prediction, remote patient monitoring, personalized treatment, anomaly detection, and clinical decision-making. Cloud platforms provide scalable storage, processing power, and seamless accessibility for healthcare data analytics. However, some challenges like data privacy, security, interoperability, data quality, and regulatory compliance remain significant. Some of the future opportunities arising from this convergence include federated learning, explainable AI, digital twins, predictive healthcare, and AI-driven precision medicine – which have the potential to improve healthcare efficiency and patient outcomes.
Today, Machine Learning (ML) plays a transformative role in IoT-cloud enabled healthcare by enabling intelligent analysis of real-time patient data collected through wearable devices, and smart medical systems. However, some challenges remain like data privacy, interoperability, data quality, and regulatory compliance remain significant.
