A scoping review of generative AI and digital twins in tackling loneliness during hospital-to-home transitions.

Abstract

Loneliness is an important psychosocial concern for older adults and may be intensified during the transition from hospital to home, when established routines, professional contact, functional capacity, and social participation can change simultaneously. This scoping review examined how generative artificial intelligence (GenAI), AI-enabled technologies, and digital twins (DTs) have been investigated in relation to loneliness, social connectedness, and psychosocial support for older adults, with particular attention to their relevance to hospital-to-home transitions. Following Joanna Briggs Institute guidance and PRISMA-ScR reporting principles, six interdisciplinary databases were searched for English-language peer-reviewed studies published between 2020 and 2026. Thirty-two primary studies were included in the final synthesis. Review-level publications were excluded from the primary synthesis and used separately to contextualize the broader technology landscape. The evidence revealed two largely separate research streams. Loneliness-focused studies predominantly investigated conversational agents, smart speakers, voice assistants, social and companion robots, large-language-model-supported systems, generative reminiscence, and AI-based loneliness detection, mainly in community, home, and long-term-care settings. In contrast, hospital-to-home studies primarily addressed continuity of care, rehabilitation, symptom communication, monitoring, personalized assistance, and service coordination, with loneliness rarely assessed as a primary outcome. Digital-twin applications relevant to loneliness were particularly limited and remained largely conceptual or design-oriented. No included study evaluated an integrated GenAI-DT intervention that continuously interpreted changing post-discharge context and used this information to personalize relational support within an accountable hospital-to-home care pathway. The principal gap is therefore not the absence of AI-enabled support, but the lack of integration between relational GenAI capabilities, context-aware sensing and digital-twin representations, and transitional-care services. Based on these findings, this review proposes a Relational Intelligence Ecosystem as an evidence-derived and theory-informed framework to guide the future design and evaluation of personalized, context-aware, and human-supervised smart services for older adults during hospital-to-home recovery.