Perceived Empathy in Chatbot Interactions for Chronic Disease Management: A Scoping Review

Chronic disease management places sustained emotional and practical demands on patients, and healthcare systems are increasingly turning to chatbot-based interventions to extend support beyond the traditional clinical encounters. Yet the role of empathy in shaping patients’ lived experience of these tools has received uneven attention: the design of empathic features has advanced faster than our understanding of how patients perceive and respond to them. This scoping review was conducted to map the current evidence on how users with chronic conditions perceive and experience empathy in chatbot-assisted disease management, and to examine the chatbot design strategies, user outcomes, and contextual factors that influence those perceptions. Following Joanna Briggs Institute methodology and PRISMA-ScR reporting guidelines, a systematic search was conducted across five databases, namely PubMed, Scopus, Web of Science, PsycINFO, and CINAHL, with the final search update conducted on August 5, 2025. Eligibility criteria were defined using the Population, Concept, and Context framework. Studies were included if they involved users managing chronic or long-term health conditions, addressed perceived empathy or empathy-related relational constructs in chatbot interactions, and were situated within healthcare or self-management contexts. Forty studies met the criteria for inclusion and were synthesized using thematic and narrative synthesis. The findings reveal that empathic design features, including personalized language, active listening cues, and emotionally responsive framing, are associated with more positive user perceptions of trust, satisfaction, and therapeutic alliance. However, the construct of digital empathy remains inconsistently operationalized across studies, with fewer than half of the included studies formally assessing empathy. User outcomes vary considerably depending on disease type, interaction modality, and the underlying chatbot architecture, with generative AI-powered systems generally associated with higher perceived empathy than earlier rule-based systems. The emergence of large language models has also begun to shift user expectations upward, such that chatbots regarded as adequate at the time of their evaluation may appear insufficient as users gain familiarity with more capable systems. This review highlights both the genuine promise of empathic chatbot design and the methodological fragmentation that limits what can be concluded from the existing evidence. It identifies priority areas for future research, including standardized measurement instruments, theory-grounded design approaches, and greater representation of underserved populations. These findings carry practical implications for developers, clinicians, and policymakers seeking to integrate conversational agents into chronic disease care in a way that is both clinically effective and relationally meaningful. Keywords: chatbot, conversational agent, empathy, chronic disease management, digital health, user experience, scoping review