Towards Conversational Patient History-Taking: Voice-Interactive AI Agents for Pre-visit Dementia Diagnostic Interviews

Patient history-taking is a critical yet time-intensive component of clinical diagnosis, frequently hindered by time-constrained clinical visits. We present an LLM-based voice-interactive conversational system for conducting semi-structured diagnostic interviews with older adults suspected of Alzheimer's disease and related dementias (ADRD). The system features conditional conversation branching over specialist-developed interview scripts and interaction adaptations tailored for older adults. In a within-subjects study with 30 participants from a cognitive neurology clinic, we compare two LLM prompting strategies through dialogue analysis, assess user experience, and evaluate symptom elicitation against routine specialist interviews. Our findings reveal that prompting strategy distinctly shapes both conversational dynamics and symptom coverage. Together with high sensitivity scores and positive user experience ratings, these results demonstrate the feasibility and clinical potential of LLM-based conversational agents for scalable, patient-centered history-taking in ADRD care.