As healthcare services deploy AI to automate patient-facing communication, concerns persist about the interactional work through which empathy is made relevant. We examine empathy not as an internal state but as an interactional accomplishment, asking how patients display orientations to an LLM-powered voice assistant’s turns as (non-)empathic in real clinical telephone calls. Using Conversation Analysis (CA) to analyse post–cataract surgery follow-up calls conducted by AI-powered voice assistant Dora (Ufonia), we compare patient responses across earlier and later system versions.Earlier calls show minimal, delayed, prosodically closed responses to wellbeing enquiries, consistent with treating Dora as a transactional information-gathering device. Later calls more often feature socially rich formats, for example colloquial upgrades, gratitude tokens, occasional return enquiries, and increased turn-final rising intonation, suggesting patients hear Dora’s talk as socially implicative and thus opening space for affiliative/empathetic uptake. We discuss implications for CA-informed conversation design and for evaluating “empathy” via participant orientations in situ rather than post-hoc self-report.