JMIR Publications

ABSTRACT

Background:

Design research on artificial intelligence (AI) technologies for mental health has expanded rapidly, yet existing work has largely focused on practitioners’ perspectives within isolated therapeutic stages or specific services. As a result, there is limited understanding of how practitioners view the role of AI across the full therapeutic journey, including assessment, diagnosis, and treatment, or how these perspectives can be translated into actionable and ethically grounded design guidance. This lack of stage-specific guidance introduces several risks: (a) the development of AI tools that practitioners may not trust or adopt; (b) heightened ethical concerns, particularly in diagnosis where bias and erosion of human judgment are most likely; and (c) missed opportunities to leverage AI to support ongoing assessment and treatment adaptation.

Objective:

This study aimed to examine mental health practitioners’ perspectives on the role of AI technologies across different stages of therapeutic practice, with particular attention to ethical considerations and design implications.

Methods:

We conducted semi-structured interviews with 18 mental health practitioners to explore how AI may support or hinder therapeutic practice across assessment, diagnosis, and treatment. Data were analysed thematically, and ethical challenges were organized using four biomedical principles, non-maleficence, beneficence, justice, and autonomy. The study received approval from the Lancaster University Institutional Ethics Committee (ID 3530).

Results:

Practitioners perceived potential value for AI across all stages of therapy in supporting data analysis and transparent decision-making under human oversight. A clear stage-specific pattern emerged, with high perceived value for assessment (38%) and treatment (55%), but strong resistance to autonomous AI involvement in diagnosis (6%), which was viewed as the most ethically sensitive stage. Practitioners emphasized that AI should support and validate clinical decisions rather than replace human judgment. Key ethical concerns included validity, inclusivity, accessibility, privacy, transparency, and explainability. Practitioners also highlighted depression-specific challenges, including gender-based diagnostic bias, age-related variation, and ethnic disparities. Based on these findings, four design directions were identified: (1) AI-enabled clinical assessment hubs for analysing multimodal patient data to support transparent decision-making; (2) natural language processing-based tools to enable richer understanding of patients’ lived experiences; (3) new models of trust to support ethically sensitive AI-assisted diagnostic decision-making; and (4) large language model-powered personalized and adaptive mental health support systems that maintain human-AI partnership and preserve practitioners’ central role in care.

Conclusions:

This study provides a stage-specific understanding of practitioners’ perspectives on AI use across assessment, diagnosis, and treatment and systematically frames ethical challenges using biomedical principles. The findings translate practitioner insights into actionable design guidance for ethically informed AI-supported mental health practice.