Next‐Generation AI/ML Algorithms for Health Monitoring

Proficient tracking of health parameters within a person has improved in accuracy over the years with the introduction of new technologies, primarily due to the development of Artificial intelligence and machine learning, which use deep learning (DL) and neural network (NN) algorithms to thoroughly examine vast datasets for diagnostic and monitoring purposes. This chapter will analyze the latest advancements in DL and NN with regard to their applications in healthcare and the potential impacts they may have in changing the face of medicine. Section 1 focuses on how sustained health maintenance through monitoring using DL and NN models may help control chronic diseases by providing real-time evaluation of data streams from smart gadgets. The traditional methods of health tracking lack the real-time aspect entirely. Then, the study describes common DL and NN architectures used in health tracking, which include long short-term memory, convolutional neural networks, and recurrent neural networks. These models have shown great performance in image classification and time-series analysis and can be used for different types of health data. Among various healthcare services, the application of DL and NN algorithms is prominent in disease diagnosis, remote patient monitoring, and management of personalized medicines, where vital sign measurement, such as heart rate, blood pressure, and glucose levels, can be analyzed to predict and diagnose health status and outcomes. The paper also discusses the necessity of having nonoverlapping discussed datasets, model explainability, ethics, and other issues while proposing some solutions on how to advance DL models to be more dependable, less complex, and more explainable.