Multidisciplinary Applications of Artificial Intelligence in Disease Prediction and Patient Monitoring

Artificial intelligence (AI) has rapidly evolved as a transformative technology in modern healthcare, offering powerful tools for disease prediction and continuous patient monitoring. By leveraging machine learning, deep learning, and advanced data analytics, AI systems can process large volumes of heterogeneous healthcare data, including electronic health records, medical imaging, physiological signals, and genomic information. These capabilities enable early disease detection, risk stratification, and real time monitoring across multiple clinical disciplines. This narrative review explores the multidisciplinary applications of AI in disease prediction and patient monitoring, highlighting its role in preventive medicine, chronic disease management, critical care, and population health surveillance. The review discusses commonly used AI methodologies, clinical applications across various specialties, and the integration of multimodal data to enhance predictive accuracy and personalized care. Additionally, key challenges related to data privacy, algorithmic bias, interpretability, and clinical implementation are examined. Understanding the current landscape and limitations of AI is essential for its safe, ethical, and effective adoption in healthcare. The review concludes that AI holds significant promise in advancing predictive and patient-centered care, provided that interdisciplinary collaboration and robust governance frameworks support its clinical integration.