Conversational AI in Community Care: Preliminary Insights from a Scoping Review

Abstract

Community-based care represents a strategic priority for healthcare systems globally, yet the integration of conversational artificial intelligence (CAI) in these settings remains underexplored. This ongoing scoping review investigates current applications of CAI in community care settings to identify and categorise functional capabilities that can guide future implementation decisions. Through systematic database searches, we identified 65 papers for detailed analysis. Our initial observation surfaced eight CAI capabilities: identify, detect, generate, create, record, send, adapt, and operate. ‘Generation’ was frequently observed to produce personalised responses, data summaries or care recommendations. ‘Adaptation’ appeared particularly relevant in community care, facilitating linguistically and culturally responsive care. Emergent insights include the role of CAI in supporting relational care, enhancing cultural and contextual sensitivity, enabling collaboration with human agents, and processing multimodal data inputs for diverse care settings. This capability-centred analysis will provide an evidence-based foundation for innovators and clinical teams to make informed decisions about CAI integration in community care environments, with implications for scaling accessible and culturally appropriate care delivery.