Smart Healthcare Systems: AI-Driven Innovations for Disease Prediction, Diagnosis, and Public Health Management

Abstract

The convergence of artificial intelligence (AI), the Internet of Medical Things (IoMT), and big data analytics has transformed healthcare delivery from a reactive to a predictive and preventive paradigm. This article examines the role of AI-driven smart healthcare systems in disease prediction, clinical diagnosis, and public health surveillance. Machine learning (ML) and deep learning (DL) architectures—including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Random Forest, and XGBoost—are analyzed for their diagnostic performance across cardiovascular disease, diabetes, cancer, and infectious disease outbreaks such as COVID-19. Comparative accuracy benchmarks, sensitivity/specificity metrics, and real-world deployment case studies are presented in tabular form. The article further discusses federated learning for privacy-preserving diagnostics, wearable-based continuous monitoring, and AI-enabled epidemic forecasting models used by public health agencies. Challenges related to data heterogeneity, algorithmic bias, interpretability, and regulatory compliance are critically discussed, followed by future research directions toward explainable AI (XAI) and edge-computing-based healthcare infrastructure.