Multilingual Voice AI for Postoperative Cataract Follow-Up in Turkish Speaking Patients in the United Kingdom: Patient and Public Involvement Focus Group Study.

Background: Conversational voice AI assistants can automate postoperative follow-up calls in high-volume, low-complexity pathways such as cataract surgery but may widen health inequalities if language access and inclusive design are not built in. This patient and public involvement focus group was conducted to inform the Turkish-language adaptation of Dora ahead of a forthcoming multilingual clinical trial at Moorfields Eye Hospital. Objective: This study aims to inform the Turkish-language adaptation of Dora by gathering input from Turkish speaking community contributors about their experiences with UK ophthalmic care, language-related barriers, and design requirements for an equitable voice AI. Methods: We conducted a 1-time, 2-hour patient and public involvement focus group with 7 Turkish speaking adults recruited via the Derman community charity. The session ran in 2 phases: contributors first discussed their experiences with UK ophthalmic care, then evaluated a prerecorded Turkish-language telephone call from a voice AI to a Turkish speaking volunteer. The session was delivered bilingually, recorded with consent, and synthesized using an approach informed by the principles of reflexive thematic analysis. The voice AI uses automatic speech recognition and neural text-to-speech, with a large language model-based dialog manager for open-ended conversation within a postoperative review protocol. Results: Contributors described how pathway delays and limited language support shape their care, including reliance on family members for translation and concerns about privacy and autonomy. A language-concordant voice AI was conditionally acceptable for standardized postoperative follow-up, provided specific safeguards were met. Priorities included advance notice of calls, caller verification, privacy assurances, a clear standard Turkish accent at a slower pace, tolerance for regional dialects, interpersonal warmth, interactivity, accessibility for low vision and low literacy, and clinician escalation for complex issues. These priorities were synthesized into a 10-point checklist: preparation, verification, confidentiality, clarity and pace, voice, empathy, interactivity, dialect handling, accessibility, and efficiency. Conclusions: For patients facing language barriers, conversational voice AI may complement existing services when implemented with clear verification, privacy protections, and a defined scope under clinician oversight. The 10-item checklist will guide the Turkish-language adaptation of Dora and will be tested alongside similar consultations with other language communities in the forthcoming multilingual cataract follow-up trial.