A scoping review on conversational AI in mental health: A human-centered perspective

Conversational AI offers scalable mental health support, with large language models (LLMs) enabling personalized interactions. Human-centered design is critical in this domain, yet a comprehensive synthesis from this perspective is lacking. This review maps conversational AI research in mental health across the patient journey and develops a human-centered taxonomy to guide future design.

Following PRISMA guidelines, we conducted a comprehensive search across fifteen multidisciplinary databases. We systematically analyzed the literature across six dimensions: research foci, mental disorder types, target populations, AI technologies, data sources, and evaluation metrics. A consensual taxonomy research method was employed to develop a human-centered design framework.

Of 10,293 identified records, 677 studies met the inclusion criteria. Analysis reveals a marked increase in publications since 2020, predominantly from computer science (449 studies), followed by medicine (148) and social sciences (80). Research is skewed toward detection (23%) and intervention (66%) stages, with prevention (8%) and maintenance (3%) receiving less attention. Mood, anxiety, and stress-related disorders are the most investigated conditions. LLMs have emerged as the predominant AI technology, particularly within intervention and maintenance stages. Data sources continue to rely heavily on text-based inputs, with multimodal approaches still limited in adoption. Evaluation metrics vary significantly by discipline, reflecting limited cross-disciplinary integration. Through thematic synthesis, we developed a human-centered taxonomy comprising four primary dimensions: Emotional Sensitivity to Users, User-Centric Interaction Design, Human-AI Collaboration and Capability Enhancement, and Ethics and Accountability, with a total of thirteen sub-dimensions.

This review provides a comprehensive, human-centered mapping of conversational AI research in mental health across the patient journey. Critical gaps remain in stage coverage, disorder diversity, population inclusivity, multimodal data integration, and interdisciplinary evaluation. The proposed taxonomy offers a structured framework to align AI development with human-centered principles, fostering empathetic, ethical, effective, and equitable mental health support.