Advancing Smart Healthcare: Integrating AI and IoT for Enhanced Patient Care and System Efficiency

Smart healthcare systems are undergoing rapid transformation through the convergence of the Internet of Things (IoT) and Artificial Intelligence (AI). Together, these technologies enable continuous patient monitoring, predictive analytics, and automated clinical decision support, promising improvements in care quality, operational efficiency, and personalization of treatment. Wearable sensors, connected medical devices, and remote monitoring platforms now generate vast streams of physiological data that, when processed with AI, can support early detection of clinical deterioration and better management of chronic diseases such as diabetes, cardiovascular conditions, and respiratory illness.

Despite this promise, significant barriers remain. Health systems struggle with data interoperability across heterogeneous devices and platforms, ensuring the security and privacy of sensitive patient data transmitted over IoT networks, and validating AI models for safe, unbiased, and generalizable clinical use. Real-time processing at scale, edge-computing constraints, and regulatory uncertainty further complicate deployment.

This Research Topic invites contributions that examine the technological, clinical, and ethical dimensions of AI-driven IoT integration in healthcare. We welcome original research, reviews, and perspective articles addressing (but not limited to):

- Architectures and enabling technologies for AI-IoT integrated healthcare systems (edge/fog/cloud computing, 5G/6G connectivity, sensor design).
- AI algorithms for real-time patient monitoring, anomaly detection, and early warning of clinical deterioration.
- Applications in chronic disease management (diabetes, cardiovascular disease, COPD, etc.).
- Data interoperability standards and frameworks (e.g., HL7 FHIR) for connected health devices.
- Privacy-preserving and secure data-sharing methods (federated learning, blockchain, encryption) for IoT health data.
- Clinical workflow integration and impact on hospital/system operational efficiency.
- Personalized and predictive treatment models driven by AI-IoT data fusion.
- Regulatory, ethical, and trust considerations for AI-enabled connected medical devices.
- Case studies and pilot deployments evaluating clinical or economic outcomes.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: IoT healthcare, artificial intelligence, remote patient monitoring, smart hospitals, chronic disease management, data interoperability, digital health security, predictive analytics, clinical decision support

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.