Deep learning used in IoT-enabled healthcare transformation to provide better patient monitoring and diagnostics

Abstract

Deep learning's and the internet of things' revolutionary capabilities are generating a dramatic shift in the healthcare sector. An examination of the possible monetary chaos that may ensue from healthcare systems implementing deep learning for patient identification and monitoring through the internet of things is undertaken in this study. Wearable sensors, smart devices, and internet-connected medical equipment have made it possible for medical personnel to monitor their patients' respirations, heart rates, and other physiological indicators in real time. But the massive amounts of complicated data produced by these gadgets make analysis and diagnosis difficult. Deep learning algorithms do a great job of sifting through this ever-growing heap of medical records. Data collected from sensors, electronic health records (EHRs), and patient reports can be automatically analyzed for complex patterns and relationships using Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Clinicians can use this ability to better diagnose patients, identify warning signals, and tailor therapies to each individual's needs. This study presents the specifics of an Internet of Things (IoT) healthcare system that employs convolutional neural networks (CNNs) and long short-term memories (LSTM) for tasks such as feature extraction, data classification, prediction, and development. When it comes to healthcare settings, using real-time updated deep learning models raises questions about interpretability, privacy, and accessible resources. The study demonstrates that the Internet of Things (IoT)—specifically, Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM)—can enhance healthcare. These systems enable optimization of therapies, real-time diagnosis of diseases, and risk predictions. Healthcare that is both accessible and inexpensive can improve with the application of these ideas. Through networked devices and sophisticated analytics, the combination of the Internet of Things (IoT), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) can significantly improve disease detection, individualized therapy, and patient monitoring.