Trustworthy and Privacy-Preserving AI for IoT-Enabled Healthcare Systems

The rapid proliferation of Internet of Things (IoT) technologies in healthcare has enabled continuous patient monitoring, wearable diagnostics, remote care, and real-time clinical decision support. These systems generate massive volumes of sensitive medical data, including physiological signals, imaging data, and behavioral patterns. While artificial intelligence (AI) enhances predictive capabilities and personalized treatment, it also introduces critical concerns related to privacy, security, data integrity, and regulatory compliance. Traditional centralized learning frameworks require aggregating patient data into cloud infrastructures, raising serious risks of data breaches, model inversion attacks, and unauthorized access. Emerging approaches such as federated learning, differential privacy, secure multi-party computation, homomorphic encryption, and edge intelligence offer promising alternatives by enabling decentralized and privacy-preserving model training. However, practical deployment in IoT healthcare environments remains challenging due to resource constraints, device heterogeneity, communication overhead, and trade-offs between privacy and utility.This Research Topic aims to bring together interdisciplinary contributions that advance trustworthy and privacy-preserving AI frameworks tailored for IoT-enabled healthcare systems. We invite submissions addressing theoretical foundations, system architectures, algorithmic innovations, and real-world deployment strategies. Contributions exploring secure edge intelligence, privacy–utility trade-offs, adversarial robustness, explainable AI in medical IoT, and regulatory-compliant solutions are particularly encouraged. By bridging AI, cybersecurity, distributed systems, and digital health, this Research Topic seeks to establish scalable, secure, and ethically responsible AI solutions for next-generation connected healthcare ecosystems.As healthcare devices become increasingly interconnected and intelligent, sensitive patient data is continuously generated and processed across distributed environments. Ensuring confidentiality, robustness, and regulatory compliance while maintaining high diagnostic performance remains critical. Addressing resource limitations, communication constraints, heterogeneous data distributions, and adversarial vulnerabilities is essential for building trustworthy AI systems in connected healthcare ecosystems. The ultimate objective is to enable secure, reliable, and ethical AI deployment in IoT-driven healthcare environments.This Research Topic is aligned with the 2026 International Conference on Secure and Intelligent Digital Ecosystems (SIDe’26) on 22–23 October 2026. Authors presenting relevant work at SIDe’26 are encouraged to submit extended versions of their contributions. Submissions are open to the broader research community and will undergo the journal’s standard independent peer-review process.2026 International Conference on Secure and Intelligent Digital Ecosystems (SIDe’26) on 22–23 October 2026 Topic editors: Dr. Hari Mohan Rai, Syed Atif Moqurrab, Abdul Razaque Source: https://www.frontiersin.org/research-topics/78880/trustworthy-and-privacy-preserving-ai-for-iot-enabled-healthcare-systems