Leveraging Artificial Intelligence and Advanced Predictive Models in Smart Health Monitoring Systems for Early Detection and Personalized Medical Interventions

Abstract

Intelligent health monitoring systems that are connected with artificial intelligence (AI) have made it feasible to manage healthcare in a proactive manner. The purpose of this study is to investigate how artificial intelligence-driven predictive analytics could improve real-time health monitoring by utilising continuous physiological data from wearable devices and other digital health sources. Although technological advancements have been made, substantial hurdles still exist, including the diversity of data, the requirements for real-time processing, the personalisation of models, issues over privacy, and interpretability. This study provides a complete artificial intelligence framework that is meant to assist early detection of health conditions and tailored risk prediction. This framework is provided by resolving the issues that have been raised. By enhancing the accuracy of forecasts and facilitating prompt medical interventions, the system intends to minimise adverse health occurrences and increase the quality of care provided to patients. The findings shed light on the growing potential of analytics driven by artificial intelligence in the development of health monitoring systems that are more intelligent and responsive, which has the potential to greatly enhance patient outcomes and the efficiency of healthcare.