AI-Driven Predictive Modeling in Chronic Disease Management

By facilitating early detection, individualized therapy, and better patient outcomes, AI-driven predictive modeling holds the potential to completely transform the management of chronic diseases. Artificial intelligence (AI) algorithms are able to recognize complex patterns and forecast an individual's future health by evaluating massive datasets from several sources, such as wearable technology, electronic health records, and demographic data.aAI-powered predictive modeling represents a breakthrough in chronic disease management by enabling early detection, personalized treatment, and improved health outcomes for patients. Algorithms rely on analyzing massive datasets from multiple sources, with edge computing, data can be analyzed locally, reducing response time and accelerating decision-making quick decision-making, Therefore, the purpose of this study is to clarify the gaps resulting from the traditional management of chronic diseases by exploring the various roles of predictive modeling based on artificial intelligence. This will be done by enumerating some of these gaps and highlighting the reasons why AI-based methods may be useful in addressing them, particularly in providing more details regarding the latest developments aimed at enhancing long-term health outcomes for patients. To achieve this, a descriptive analytical approach was adopted to study and analyze the current technologies used, along with conducting an in-depth analysis of the impacts these technologies may have on health outcomes. Keywords: AI-driven healthcare, predictive analytics, chronic disease management, personalized treatment, data privacy.