The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
Abstract
Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential benefits in improving mental and emotional well-being, addressing specific mental health conditions, and facilitating behavior change. However, challenges related to usability, engagement, and integration with existing healthcare systems were identified. AI chatbots hold promise for mental health interventions, but widespread adoption hinges on improving usability, engagement, and integration with healthcare systems. Enhancing personalization and context-specific adaptation is key. Future research should focus on large-scale trials, optimal human–AI integration, and addressing ethical and social implications.
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Systematic reviews broadly support the potential of CBT-inspired chatbots to assist individuals with mild-to-moderate mental health conditions, though they call for more rigorous long-term evaluation and clearer clinical governance frameworks.
“Systematic reviews broadly support the potential of CBT-inspired chatbots to assist individuals with mild-to-moderate mental health conditions, though they also call for more rigorous long-term evaluation and clearer clinical governance frameworks (Balcombe, 2023;Casu et al., 2024;Gallegos et al., 2024;Kwon et al., 2024).”
From Artificial intelligence and unconditional love: the rise of generative AI as an alternative form of mental health support (2026) · cites this paper as Casu et al., 2024
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“…also complement existing treatment modalities, serving as adjuncts to human therapists or self-help tools. by He et al. [58] and Prochaska et al. [64], utilized standardized tools such as the Working Alliance Questionnaire (WAQ While the studies indicate the feasibility and acceptability of AI…”
InfluentialHe, Y., Yang, L., Zhu, X., Wu, B., Zhang, S., Qian, C., & Tian, T. (2022) · Journal of Medical Internet Research · doi:10.2196/40719 · Cited by 178
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“Web-based psychotherapeutic interventions have proven effective for common mental health disorders such as depression, anxiety, substance abuse, and eating disorders [13].”
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
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“Bennion et al. [71] explored the use of web-based conversational agents to facilitate problem-solving among older adults.”
InfluentialUlrich, S., Gantenbein, A. R., Zuber, V., Von Wyl, A., Kowatsch, T., & Künzli, H. (2024) · Journal of Medical Internet Research · doi:10.2196/50132 · Cited by 36 · cited 3×
“Similarly, Ulrich et al. [62] noted good engagement rates with a substantial portion of participants completing the program, and Vereschagin et al. [63] found higher engagement with the chatbot compared to other app components.”
Casu, M., Guarnera, L., Caponnetto, P., & Battiato, S. (2024) · Forensic Science International: Digital Investigation · doi:10.1016/j.fsidi.2024.301795 · Cited by 30
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Cited by
Sorted by citations
Yuan, Y., Zhang, J., Aledavood, T., Zhang, R., & Saha, K. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790558 · Cited by 26
Zhang, H., Feng, Y., Walker, A., & Rode, J. A. (2025) · Proceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3663547.3746363 · Cited by 3
“AI-driven chatbots, such as Woebot and Wysa, have also emerged as digital mental health companions, providing conversational support based on CBT principles to assist users in managing stress and anxiety [21].”
Unlocking AI Chatbot Potential in Healthcare: Trust-Enhanced DeLone & McLean IS Success Model
Sarhan, M. Y., Alarify, M., & Khojah, M. (2026) · Healthcare · doi:10.3390/healthcare14101324 · Cited by 2
“…vaccine hesitancy [16–19] improving diabetic patient education [20,21], providing emotional support for patient [22,23], assisting in remote mental health monitoring [24–26], aiding tobacco cessation [27–30], enhancing drug compliance, and alleviating symptoms of depression and anxiety [7,31,32].”
Miller, V., Hill, M. J., & Moreira, T. (2026) · Communication and Change · doi:10.1007/s44382-026-00036-1 · Cited by 2
Metadata
- Authors
- Casu, M., Triscari, S., Battiato, S., Guarnera, L., & Caponnetto, P.
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2024-07-05
- Volume
- 14
- Issue
- 13
- Pages
- 5889
- ISSN
- 2076-3417
- Cited by
- 186
- References
- 17
- Language
- en
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