Generative AI-Driven Multimodal Interaction Design for Age-Friendly Health Consultation

Abstract

With an increasingly aging global population and rising demand for digital health solutions, elderly health service management faces numerous critical challenges—including delayed health data monitoring, insufficient personalization of health recommendations, and persistent digital divides in technology adoption. This study focuses on the fundamental self-health assessment and consultation needs of older adults, employing an AIGC-based multimodal model as its core technical enabler. It systematically investigates interaction design principles for an AI-powered chatbot specifically tailored to support elderly users’ health-related inquiries. Through analysis of key using scenarios and functional requirements associated with elderly self-health assessments and consultations, this research proposes a Multimodal AIGC Interaction Design Framework for Older Adults. Grounded in AIGC-driven multimodal interaction, empathetic emotional communication, and age-inclusive design principles, the framework supports personalized interpretation of individual physical examination reports, image-based daily dietary analysis, and holistic, individualized planning of healthy lifestyles. This research aims to enhance both the operational efficiency，user experience and service quality of AI-enabled health screening and consultation chatbots for older adults. Furthermore, it contributes actionable methodologies and practical design tools to advance the development of age-friendly AI health services.

Keywords: Generative AI, Health Service, Design For Older Adults, Multimodal Interaction

DOI: 10.54941/ahfe1008050