JMIR Publications

ABSTRACT

Background:

Design research on AI technologies for mental health has grown substantially, but prior work often focuses on practitioners’ perspectives within a single therapeutic stage or specific service. Less research has mapped how practitioners view AI across the main stages of therapy (assessment, diagnosis, and treatment) or translated their perspectives into actionable, ethically grounded design guidance. To address this gap, we conducted interviews with 18 mental health practitioners to explore how AI may support or hinder therapeutic practice, with attention to ethical considerations. Findings show that practitioners see potential for AI across all therapy stages to aid data analysis and transparent decision-making, under human oversight. Notably, there is a stage-specific pattern: high value for assessment (38%) and treatment (55%), but strong resistance to autonomous AI diagnosis (6%), which is considered the most ethically sensitive stage. Practitioners emphasized that AI should validate and support decisions rather than replace human judgment. Ethical challenges were organized using four biomedical principles: non-maleficence, beneficence, justice, and autonomy. Key concerns include validity, inclusivity, accessibility, privacy, transparency, and explainability. Practitioners highlighted depression-specific challenges, such as gender-based diagnostic bias, age-related variations, and ethnic disparities. Our paper addresses these gaps by: (1) providing a stage-specific analysis of AI support within and across assessment, diagnosis, and treatment; (2) systematically framing ethical concerns using biomedical principles; and (3) translating practitioner insights into design implications. We propose three design directions: (1) clinical assessment hubs using AI to analyze multimodal patient data for transparent decision-making; (2) NLP-AI tools for richer understanding of patients’ experiences; and (3) LLM-powered personalized, adaptive mental health support systems that maintain human-AI partnership and practitioners’ central role.

Objective:

To explore mental health practitioners' perspectives on AI technologies across the main stages of therapeutic practice (assessment, diagnosis, and treatment) and identify stage-specific ethical challenges mapped to biomedical ethics principles.

Methods:

We conducted semi-structured interviews with 18 mental health practitioners (16 female, 2 male; mean experience = 9 years) in the UK. Participants were accredited members of the National Association for Counselling and Psychotherapy. Each interview began with a 10-minute presentation on AI concepts and a scenario involving an AI system for depression detection. Interviews lasted 48-65 minutes and were conducted via Microsoft Teams. Data were analyzed using hybrid coding (deductive and inductive) with ATLAS.ti software, applying biomedical ethics principles (non-maleficence, beneficence, justice, autonomy) and therapeutic stages as main codes. Over 1,130 codes were generated through iterative analysis.

Results:

Practitioners assigned different levels of value to AI across therapeutic stages: treatment (55%), clinical assessment (38%), and diagnosis (6%). They emphasized that AI should validate and support practitioners' decisions rather than operate autonomously, particularly for diagnosis, the most ethically sensitive stage. Practitioners saw value in AI for gathering and analyzing multimodal patient data, recommending personalized interventions, monitoring treatment effectiveness, and supporting patient engagement. Ethical challenges were organized under four principles: non-maleficence (concerns about limited data validity and potential harm), beneficence (need for comprehensive assessment tools and therapeutic rapport), justice (inclusivity, accessibility, and diversity in AI systems), and autonomy (privacy, transparency, and explainability of AI decisions). Depression-specific challenges included gender-based diagnostic bias, age-related symptom variations, and ethnic disparities in diagnosis.

Conclusions:

Mental health practitioners view AI as valuable when integrated as a supportive tool under human oversight within a human-AI partnership model. We propose four design directions: clinical assessment hubs integrating multimodal data, NLP-AI tools for deeper understanding of patient experiences, co-designed trust models positioning AI as a validator of diagnostic judgment, and LLM-powered personalized support systems maintaining practitioner oversight.