Real-time Predictive Health Monitoring using Ai-driven Wearable Sensors: Enhancing Early Detection and Personalized Interventions in Chronic Disease Management

The use of artificial Intelligence in wearable health sensor has brought a great change in the way chronic diseases are being managed since it monitors the health and gives prompt prediction of diseases coupled with recommendations. This paper seeks to investigate the part played by AI wearables in augmenting early detection & proactive plans relating to chronic illnesses, including diabetics, cardiovascular diseases, and hypertension. Implicitly, the purpose of this study is to update the current understanding of the subject by examining the effect that predictive analytics has on patient outcomes and cost. Technologically, the concepts of the research rely on the data collected from different kinds of wearable devices and analyze by machine learning and real time analytic to examine the abnormal of vital signs before symptoms occur. Analysis is carried out on the outcomes which reveals a 25 percent decrease in hospitalization incidences per early diagnosis of chronic diseases and a 30 percent enhancement in the adherence to treatment provided formed by precautions embraced from various sensors data. This research also increases the stock of knowledge on the role of artificial intelligence in healthcare by presenting empirical evidence regarding the use of wearable devices in chronic diseases. The uniqueness of this concept is in the synchrony of monitoring processes with the application of the predictive model in providing proactive health care solutions. Some potential future research and development considerations in AI-assisted technologies in healthcare are also described.