# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
## 4 Discussion
### Limitations and Future Research Directions  
This scoping review has several limitations. Future systematic reviews and meta-analyses targeting specific application areas or population groups could offer more rigorous evaluations of the evidence. Furthermore, the rapid evolution of AI technology suggests that the findings of this review may quickly become outdated as new advancements in natural language processing and conversational AI emerge.  
Future research should prioritize large-scale, well-designed randomized controlled trials to evaluate the long-term efficacy and cost-effectiveness of AI chatbot interventions compared to standard care or other active treatments. Studies investigating the optimal combination of human and AI-based support, as well as the integration of chatbots into existing healthcare systems, are also warranted. Also, the development of effective AI chatbots for mental health is inherently interdisciplinary, requiring collaboration between computer scientists, psychologists, healthcare professionals, and ethicists. Creating platforms for interdisciplinary research and dialogue can facilitate the integration of diverse perspectives and expertise, leading to more holistic and effective chatbot solutions. Furthermore, the deployment of AI chatbots in mental health settings raises significant ethical and security concerns.  
Ensuring data privacy and protecting user information against breaches are paramount. However, the ethical concerns surrounding relational agents like chatbots go beyond data privacy. [PERSON] [75] identifies key issues: affective privacy, emotion induction, and virtual relationships. Affective privacy relates to keeping thoughts and emotions private, raising questions about the extent of chatbot probing. Emotion induction refers to the chatbots' potential to manipulate users' emotions, bringing up consent and impact concerns. Virtual relationships, where users bond with chatbots, also blur the lines between human and artificial connections, leading to dependency worries. [PERSON] [76] reinforces these concerns with survey data showing user discomfort with AI's handling of emotions and personal data. The respondents stressed the importance of transparency and user control. Developing ethical guidelines and frameworks, as well as implementing advanced security measures such as end-to-end encryption, is necessary to address these concerns. Furthermore, as these AI systems become more sophisticated in mimicking human conversation, patients using online text-based mental health services may experience heightened doubt about the authenticity of their interlocutor. This uncertainty can manifest as a persistent suspicion that they might be interacting with an AI rather than a human therapist, even when engaging with a real person. Such doubt could potentially undermine the therapeutic relationship, affecting the patient's trust, openness, and overall treatment efficacy. Moreover, if patients become aware that some services use AI chatbots, they might extend this skepticism to all online mental health interactions, creating a broader trust issue in digital mental health services. This could be interpreted as an extension of the recently described Impostor Bias to mental health contexts [77], which further underscores the need for transparency in AI use and the importance of maintaining clear distinctions between human and AI-driven interactions in sensitive fields like mental health care.