Διερεύνηση των αντιλήψεων και προτιμήσεων των πολιτών σχετικά με τη χρήση της τεχνητής νοημοσύνης στην ψυχική υγεία
An assessment of public perceptions and preferences toward the use of artificial intelligence in mental health care
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Keywords
Τεχνητή νοημοσύνη ; Ψυχική υγεία ; Αποδοχή τεχνολογίας ; Ψηφιακή ψυχική υγεία ; Chatbots ; Αποδοχή λειτουργιών εφαρμογής ; Artificial intelligence ; Mental health ; Technology acceptance ; Feature / function acceptance ; Digital mental healthAbstract
This thesis explores the role of Artificial Intelligence (AI) in mental health not simply as another digital tool, but as a technology that may reshape how difficulties are identified, supported, and managed within care systems. The literature review is structured around three pillars. First, it outlines how AI may detect or monitor mental health-related signals through two complementary pathways: behavioral/digital traces (sleep, activity, language and interaction patterns) and neurobiological/neurological signals (biomarkers, neuro-data and clinical information). Second, it examines what drives acceptance and trust in technology, why people perceive value, what makes AI feel reliable, and when concerns emerge. Third, it discusses ethics and governance, focusing on privacy, transparency, user control, and safeguards. The empirical component is a quantitative cross-sectional online survey using a structured questionnaire. The study captures public perceptions of AI mental health applications, preferred features, conditions for use, and factors related to intention to adopt such tools. Findings suggest that acceptance is not only about liking the idea of AI, but about whether people can picture it in clear, practical use-cases. When AI is linked to concrete, everyday functions, such as mood tracking, reminders, basic well-being exercises, or early guidance supported by human oversight, intention to use becomes stronger. Familiarity with AI also acts as a bridge, easing uncertainty and supporting the step from general approval to actual willingness to try. At the same time, a clear boundary emerges around data. In mental health, technology is evaluated as a relationship of trust: what is collected, where it is stored, how long it is kept, and who can access it. Many respondents show strong caution, signaling that adoption depends on meaningful control, not just promised benefits. Overall, the study indicates a broadly positive openness toward AI in mental health, especially when usefulness is tangible. Yet this positivity does not necessarily mean that the deeper risks of highly sensitive data and potential commercial misuse are fully recognized. Responsible adoption therefore requires conditions: transparency, genuine user control, strong safeguards, and a clear commitment to maximizing human benefit rather than prioritizing costcutting or profit-driven uses of personal information.