Mobile Applications Powered by AI for Early Detection of Chronic Disease

Abstract

Artificial intelligence (AI)-driven smartphone applications, utilizing state-of-the-art technology like natural language processing, deep learning, and machine learning, are transforming the early diagnosis and treatment of chronic illnesses. These applications enable continuous health monitoring and extensive insights into individual health states through the use of modern sensors, real-time data analytics, and the Internet of Things (IoT). This analysis examines the state of AI technologies in mobile health applications today, emphasizing how useful they are for cost-cutting, personalized health monitoring, and early disease diagnosis. The advantages of better diagnosis accuracy, prompt interventions, and increased patient involvement as well as the drawbacks of data privacy, legal compliance, algorithm bias, and the requirement for ongoing updates are explored. Significant developments in data interoperability, sensor technology, and AI algorithms are anticipated in the future, which will expand the potential of these applications. Computer-based intelligence-controlled portable well-being applications can alter medical care by handling recent concerns through cooperation. They could expand openness, proficiency, and adequacy of early persistent disease distinguishing proof, which would ultimately work on tolerant results and personal satisfaction.