Amaca Özel Yapay Zeka Destek Uygulamasının Piyasadan Çekilmesinin Ardından Kullanıcılar Genel Yapay Zekaya veya Hiçbir Desteğe Yöneldi

Özet (abstract)

BACKGROUND Purpose-built AI well-being tools—designed with clinical safeguards for everyday emotional difficulties such as stress, grief, and relationship conflict—represent a growing category of digital mental health support. When such tools are removed from the market, users must navigate alternatives on their own; however, no empirical data exist on where they actually turn. OBJECTIVE We aimed to characterize real-world support-seeking after the withdrawal of a purpose-built AI well-being mobile app (Ash), specifically to (1) document where former users turned for emotional and mental health support, (2) characterize how general-purpose AI tools were used for mental health, (3) assess perceived changes in service access and subjective well-being, and (4) capture qualitative experiences of the transition. METHODS We conducted a cross-sectional survey of 393 UK-based adults who had used Ash within 90 days before its withdrawal from the UK market in January 2026. Of 7415 randomly sampled eligible users invited by email, 393 (response rate 5.3%) responded and 364 (92.6%) completed all items. The survey assessed self-reported sources of emotional and mental health support following withdrawal, use of general-purpose AI tools, perceived changes in access to support, and subjective well-being. Free-text responses (n=272) were analyzed using codebook-based qualitative content analysis, with the codebook developed through a hybrid inductive-deductive process. Reporting follows the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines. RESULTS The most common primary sources of mental health support since losing access to a purpose-built AI well-being application were general-purpose AI tools (27.1%, 104/384; 95% CI 22.9-31.7) and no support at all (26%, 100/384; 95% CI 21.9-30.7). Relatively fewer respondents reported turning to National Health Service services (19/384, 4.9%; 95% CI 3.2-7.6) or a private therapist (8.1%, 31/384; 95% CI 5.7-11.2) as their primary resource. Among the 157 respondents reporting use of general-purpose AI for mental health support, the dominant motivations were instant access (78.3%, 123/157) and cost (73.9%, 116/157) rather than perceived helpfulness (18.5%, 29/157). Overall, 60.2% (219/364; 95% CI 55.1-65.1) reported worse access to support, rising to 80.7% (71/88; 95% CI 71.2-87.6) among those who had not sought support since. Qualitative themes converged on these patterns: 33.8% (92/272) reported finding no suitable alternative, 22.8% (62/272) judged general-purpose AI inferior, and 15.8% (43/272) expressed emotional distress at the loss. CONCLUSIONS In this exploratory, retrospective survey, removing a purpose-built AI well-being tool was not associated with a redirection to human-led mental health services; most respondents turned to general-purpose AI or stopped seeking support entirely, and those who disengaged reported the poorest well-being. Regulatory frameworks should account for real-world care-seeking behavior when evaluating the risks and benefits of purpose-built AI tools for general wellness. CLINICALTRIAL

Yazarların özeti; kaynağından alınmıştır. JMIR Medical Informatics, 2026 · DOI ↗