A mixed methods analysis of health literacy among adults with brachial plexus birth injury.

In recent years, artificial intelligence (AI) technologies—particularly large language models (LLMs), conversational AI, and digital human systems—have advanced rapidly, opening new possibilities for automated interaction services. Within enterprise and healthcare settings, frontline staff are often required to handle highly repetitive and routine tasks, which may lead to inefficient use of human resources and inconsistent service quality. In hospitals, reception and triage nurses often respond to large volumes of basic inquiries and consultations over extended periods, resulting in increased workload, emotional fatigue, and operational pressure. Moreover, hospitals are high-risk environments in which frontline personnel may face elevated exposure to infectious diseases. These challenges highlight the need for AI systems that can support frontline consultation workflows by providing consistent and context-aware informational assistance. Importantly, the system proposed in this study is intended as informational support rather than as a diagnostic or triage decision-making tool. When designing AI-based customer service systems for real-world physical environments, recent studies emphasize three essential technological foundations: , , and . These elements are critical for developing service solutions that are efficient, adaptive, and user centered. However, traditional Product–Service Systems (PSS) — which integrate physical products with service processes to deliver holistic value—often struggle to incorporate user-generated data and AI-driven decision mechanisms into a coherent, practical system architecture. Therefore, a successful AI-based digital service robot must simultaneously integrate (1) data-driven design principles, (2) robust AI model utilization, and (3) human-centered interaction mechanisms to create an intelligent service system capable of responsive and context-aware operation. To ensure that physical products and associated service processes function as a sustainable solution, this study adopts a Smart Product–Service System (Smart PSS) perspective, integrating AI modules, embedded hardware, and interaction workflows into a unified framework that supports practical deployment.