Sensors to Insights Revolution: Leveraging IoT, AI, and Big Data for Next-Gen Patient Monitoring

The integration of big data analytics with the Internet of Things (IoT) has resulted in new developments in the healthcare industry. This has made data-driven decision-making, real-time diagnosis, and ongoing patient monitoring possible. It looks at how IoT is changing healthcare, emphasizing the role of medical super sensors (MSS) and wireless body sensor (WBSN) networks in gathering and evaluating practical, environmental, and physical data. A vast amount of odd data has been generated by the spread of IoT devices, opening up possibilities for improved patient outcomes, personalized treatment, and illness prediction. However, problems with data security, privacy, and interoperability persist. Long short-term memory (LSTM), deep belief networks (DBN), and convolutional neural networks (CNN) are examples of advanced machine learning (ML) algorithms that draw insightful information from complex datasets, enhancing clinical accuracy and enabling vital real-time health monitoring. Significant advancements were noted in the study, including a 30% drop in hospital admissions, a 40% rise in data availability via cloud platforms, and an 85% increase in clinical accuracy using AI-powered models. Notwithstanding these achievements, the study highlights the necessity of robust security measures and scalable, energy-efficient solutions to address the ongoing issues.