The rapid growth of 6G-enabled intelligent healthcare has created opportunities for continuous athlete health monitoring; however, existing approaches primarily emphasize physiological data analysis while lacking autonomous service orchestration, resulting in delayed healthcare response, inefficient resource management, and limited adaptability. To address these limitations, this study proposes an Artificial Intelligence-Driven Zero-Touch Digital Service Loop Orchestration (AI-ZDSLO) framework that integrates EfficientNet-B0 for deep physiological feature extraction, a Dynamic Graph Attention Network (DGAT) for adaptive health prediction, and an intelligent orchestration engine for autonomous 6G resource allocation, network slice selection, workflow prioritization, and real-time healthcare service delivery within a continuous closed-loop architecture. The framework is evaluated using publicly available wearable physiological datasets, where multimodal sensor signals undergo adaptive preprocessing before deep feature learning and dynamic graph-based prediction. Experimental analysis demonstrates the effectiveness of the proposed framework, achieving an accuracy of 99.03%, precision of 98.95%, recall of 98.88%, F1-score of 98.91%, and specificity of 99.14%, confirming its capability to provide accurate health prediction and intelligent zero-touch healthcare management for next-generation athlete monitoring.
Increasing use of embodied AI and autonomous medical devices is transforming smart hospital IIoT/IIoMT into highly connected cyber-physical ecosystems. Unlike conventional healthcare authorization systems, smart hospitals require trustworthy governance and collaborative embodied agents coordinating communication, sensing, data access, and physical actuation under strict safety, accountability, and privacy requirements. However, available techniques heavily rely on centralized decision-making and static access control policies that limit the abilities of those approaches to address privacy, safety, dynamic trust, and accountability requirements. This article proposes a trust-adaptive explainable consensus and permissioned governance framework for collaborative embodied AI in smart hospital IIoT/IIoMT networks. The proposed framework incorporates specialized governance agents responsible for policy compliance, preserving privacy, clinical validation of health conditions, risk assessment, and safety evaluation. A trust-aware explainable consensus approach aggregates the agents’ recommendations for supporting accountable and transparent decision-making, while a human oversight module handles high-risk and uncertain situations. For ensuring governance integrity and auditability, decision metadata and trust records are stored on a permissioned blockchain. An adaptive trust management engine evaluates the reliability of the participating devices and agents based on governance and operational behavior outcomes: supported by experimental evaluation and validation.
