Generative AI‐Driven Patient Twins

Abstract

The convergence of machine learning (ML), artificial intelligence (AI), and edge computing is reshaping the landscape of healthcare decision support systems. Traditionally, healthcare analytics have relied heavily on cloud-based infrastructures, latency, privacy concerns, and dependence on continuous connectivity. Edge computing, by enabling data processing directly on wearable devices, offers a promising alternative for real-time, secure, and context-aware medical interventions. This chapter delves AI and ML applications for decision creation in healthcare, such as remote patient monitoring, disease detection at an early stage, and emergency response systems. It provides an existing edge hardware and software technologies, examines deploying edge AI in healthcare settings, and surveys compelling use cases with clinical impact. It also includes issues in patient privacy, computational limitations, model interpretability, and clinical verification. AI explainability in federated learning, along with compatibility with new 5G networks, will enable secure, transparent, and efficient healthcare solutions. This paradigm will deliver intelligent, timely, and customized healthcare in remote locations through the fusion of edge computing and advanced analytics.