Background: Depression is a pervasive global mental health issue, yet access to trained professionals remains severely limited. With the rapid advancement of artificial intelligence (AI), digital tools are increasingly seen as a viable way to address this shortage. However, questions remain about how digital platforms for mental health care can be effectively designed. Objective: This study aimed to investigate, from an end user’s (patient’s) perspective, the potential use scenarios, desired features, and perceived risks of AI psychotherapists in depression treatment, providing design guidelines for their development. Methods: A grounded theory approach was applied to analyze qualitative responses from 452 individuals recruited via Amazon Mechanical Turk. Data were collected through a scenario-based online survey on AI-assisted depression treatment administered between March 2023 and May 2023. Participants responded to 3 open-ended questions regarding the potential use of AI in treating depression, the characteristics expected from an AI psychotherapist, and the associated perceived risks, along with demographic, control, and contextual measures. The open-ended responses were inductively coded into themes, with intercoder reliability established (Cohen κ=0.80). In addition, variations in themes were further examined across participant profiles, including social stigma, current depression severity, trust in an AI psychotherapist, and privacy awareness. Results: Participants envisioned AI psychotherapists across 5 primary scenarios: diagnosis, treatment, consultation, self-management, and companionship. Key desired features include professionalism, warmth, precision care, empathy, remote services, active listener, personalization, flexible treatment options, patience, trustworthiness, and basic treatment alternative, while critical concerns include diagnostic inaccuracy, treatment errors, privacy breach, lack of human interaction, technical malfunctions, and lack of emotional engagement. Based on these findings, a general MoSCoW (must have, should have, could have, and won’t have) prioritization framework was proposed to serve as a conceptual starting point for future AI system design and empirical validation in mental health care. Notably, feature prioritization varied across user profiles: individuals with higher stigma placed greater emphasis on privacy protection, those with more severe depression prioritized precision care and timely access, low-trust users de-emphasized remote services, and privacy-sensitive individuals showed reduced preference for features requiring extensive data disclosure. These patterns highlight the need for context-sensitive design. Conclusions: This study provides a patient-centered framework for designing AI psychotherapists and complements the existing literature by highlighting the importance of balancing clinical effectiveness with relational considerations. The findings offer actionable guidelines for designing AI mental health care tools that are aligned with user expectations and sensitive to individual differences.
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OTX-202 Smartphone App to Reduce Suicidal Ideation Among High-Risk Transition-Age Youth: Open-Label, Single-Arm, Phase 1 Clinical Trial
Timing of exercise differentially modulates fear memory and hippocampal neurotransmitters in male rats
Meta-analysis of the effects of exercise intervention on physical health in individuals undergoing compulsory isolation
Tryptophan modulates the impact of prolactin on insomnia in perimenopausal women: a cross-sectional study
Research trends and knowledge mapping of transcranial direct current stimulation in depression: a bibliometric study based on web of science, Scopus, and PubMed (2000-2025)
Case Report: Prism for PTSD in severe traumatic brain injury with psychiatric comorbidities: two cases
STAT+: OxyContin maker Purdue Pharma set to dissolve after judge approves its criminal sentence
NEWARK, N.J. — OxyContin maker Purdue Pharma is set to be dissolved and replaced by a company focused on the public good by the week’s end, as a massive legal settlement resolving thousands of lawsuits takes effect.
A federal judge on Tuesday delivered a criminal sentence to the company to resolve a Department of Justice probe — a last necessary step to clear the way for the settlement.
U.S. District Judge Madeline Cox Arleo made her decision after listening to hours of impact statements from people who lost loved ones or struggled with addiction themselves and requested she reject the negotiated sentence. While she didn’t go that far, she said she sympathized with people who bore the brunt of an epidemic linked to more than 900,000 deaths in the U.S. since 1999.
Implications of Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RAs) for Mood Disorders and Suicide Risk
A strategic imperative in mood disorders is to identify innovative mechanisms that translate into improved therapeutics when compared to the extant options. More specifically, there is a need for treatments with greater efficacy, shorter time-to-peak efficacy, greater durability of effect as well as improved tolerability profiles. Moreover, priority has also shifted towards identifying mood disorder therapeutics capable of targeting domains of psychopathology that are most pervasive, debilitating and inadequately treated by conventional pharmacology (e.g., anhedonia, cognitive impairment).
Current Landscape of Mental Health Conversational Agents From a Trauma-Informed Care Lens: Scoping Review
Background: Conversational agents (CAs) are increasingly used in mental health care to enhance access and engagement. However, their safe, ethical, and user-sensitive design remains a challenge. Despite growing attention to trauma-informed approaches in human-computer interaction, there is limited work on how the trauma-informed care (TIC) framework could be applied in the design of mental health CAs and no comprehensive synthesis to date. Objective: Guided by the Substance Abuse and Mental Health Services Administration’s TIC framework, this scoping review explored how TIC principles (safety; trustworthiness and transparency; collaboration and mutuality; empowerment, voice, and choice; peer support; and cultural, historical, and gender issues) are currently represented in the design and evaluation of mental health conversational agents (MHCAs) and identified gaps and opportunities to promote more trauma-informed design practices. Methods: Online databases, as well as a secondary survey of citation lists from an initial search, were used to identify English-language journal articles and conference proceedings from 2000 to 2024 that empirically evaluated an independent, web- or app-based, unassisted CA used for mental health and included concepts from TIC. Results: Our analysis included 38 publications (n=28, 73.7%, published in 2020 or later) covering 28 distinct MHCAs. Most studies used experimental methods (n=23, 60.6%) or user studies (n=11, 28.9%), with samples skewed toward female (men: mean 34.92%, SD 18.64%), young in age (mean 32.52, SD 14.6 y), and predominantly nonclinical (n=29, 76.3%). MHCAs were largely rule-based prototypes. No studies explicitly referenced the TIC framework as a guiding lens for MHCA design or evaluation. A total of 26 studies referenced terminology from TIC core principles but rarely defined them, while all 38 included language that could be linked to one or more principles. Overall, TIC-related concepts appeared most often within intervention design descriptions, qualitative assessments, or as items embedded in questionnaires evaluating broader constructs. Trustworthiness and transparency, safety, empowerment, voice and choice, and collaboration and mutuality were comparatively well addressed, while peer support and cultural, historical, and gender issues were largely absent. Design recommendations, where present, were relatively broad and emphasized secure, customizable, reliable, human-like, and context-sensitive MHCAs that offered multimodal interaction, goal setting and tracking, and transparency. Conclusions: Studies did not self-identify as using Substance Abuse and Mental Health Services Administration’s framework for TIC, making it more difficult to identify its elements. The fragmented terms, disciplines, and metrics used make it difficult to draw more systematic conclusions about the current research landscape related to TIC, but our analysis indicates TIC to be a descriptive and potentially unifying framework and provides a starting point for the explicit trauma-informed MHCA research and design.
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