AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as wearable sensors, wireless connectivity, cloud and edge computing, and advanced artificial intelligence (AI) frameworks. Special attention is given to the application of Foundational AI Tools, Generative AI ecosystem components (GenAI Stack), and TinyML, which have empowered intelligent data processing and low‐power on‐device inference in resource‐constrained environments. We explore the architecture, enabling components, and communication protocols that underpin IoT‐ECG systems, along with their security and privacy challenges. Moreover, we discuss the role of Blockchain, federated learning, and homomorphic encryption in safeguarding patient data, while also examining novel diagnostic approaches using retinal imaging, facial recognition, speech analysis, and emotional state monitoring. This review identifies existing research gaps and emphasizes the need for interoperability, energy‐efficient design, and personalized analytics in future deployments. The survey also aligns with the objectives of Sustainable Development Goals (SDG 3, 9, and 16), advocating for inclusive, innovative, and secure healthcare solutions.