The Adoption of AI in Mental Health Care–Perspectives From Mental Health Professionals: Qualitative Descriptive Study
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Abstract
Background: Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. Objective: A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. Methods: A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. Results: A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. Conclusions: AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study’s findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.
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From a patient standpoint, lack of human presence was seen as a limitation due to lack of empathy and emotional connectivity or absence of a human physician to verbally communicate and discuss.
“From a patient standpoint, the lack of human presence was seen as a limitation due to a lack of empathy and emotional connectivity with another human or simply not having the presence of a human physician to verbally communicate and discuss, such as when using an AI app or chatbot [51,64].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [51]
In Results, Theme 1 (page not provided), mental health professionals describe human connection as crucial to care and worry that AI could undermine the client–therapist relationship. The study reports professionals’ concerns about how clients might respond, not patients’ own accounts; it does not specifically identify a lack of empathy or the inability to speak with a human physician as limitations of an app or chatbot. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLack of and uncertainty surrounding funding are barriers to implementation.
“The lack of and uncertainty surrounding funding are presented as barriers to implementation [32,51,56].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [51]
In Results, Theme 2, the paper reports that participants viewed limited funding as a significant barrier to adopting AI in mental health care. It says funding varies by practice setting, but does not identify uncertainty about funding as a separate barrier. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSkepticism and a lack of understanding were seen as barriers to AI adoption.
“Skepticism and a lack of understanding were seen as barriers to AI adoption [48,51].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [51]
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Güler, K. G., & Albayrak, E. (2025) · Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi · doi:10.37989/gumussagbil.1508783 · Cited by 3 · see citing works
Integrating AI-Powered Personalization via Neurobiologically Informed DBT in Smart Wellness Resorts
Gkintoni, E., Telonis, G., Tsimakis, A., Halkiopoulos, C., & Boutsinas, B. (2025) · Springer proceedings in business and economics · doi:10.1007/978-3-031-78471-2_40 · Cited by 3 · see citing works
Samiun, M., Rony, M. K. K., Yeasmin, S., Manik, M. M. T. G., Debnath, A., Aziz, M. B., Tasnim, A. F., & Nilima, S. I. (2025) · Discover Public Health · doi:10.1186/s12982-025-00814-0 · Cited by 3 · see citing works
“Resistance to adopting new technologies, inadequate training, and fears of AI replacing human clinicians have been identified as key barriers [39].”
Evaluation of AI-based mental health interventions using the grey relational analysis
Anjum, R., Daud, S., Bhatti, G. A., & Saddique, F. (2025) · Grey Systems Theory and Application · doi:10.1108/gs-03-2025-0031 · Cited by 2 · see citing works
Ferrarini, F., & Leonelli, S. (2025) · Industrial Management & Data Systems · doi:10.1108/imds-05-2025-0661 · Cited by 3 · see citing works
Integrating Artificial Intelligence in Systemic Therapy: Strategies for Effective Clinical Practice
Hertlein, K. M., & Rajaei, A. (2026) · International Journal of Systemic Therapy · doi:10.1080/2692398x.2026.2633648 · Cited by 2 · see citing works
“In addition, the systems will operate on the biases on which they were trained which could mean unequal treatment from those of marginalized backgrounds (Zhang et al., 2023).”
Artificial Intelligence–Driven Tools in Mental Health Service Delivery: A Scoping Review
WOO, Y., & Jung, K. (2026) · Healthcare · doi:10.3390/healthcare14070943 · Cited by 2 · see citing works
“Existing research has primarily focused on attitudes, readiness, and technology-centered evaluations, emphasizing model accuracy and algorithmic performance [9,10].”
Overcoming the Potential Drawbacks of Artificial Intelligence in Psychotherapy: Literature Updates
Agazie, O., Ezema, E. C., Meftah, A., Aribisala, B., Sultana, T., Ezenagu, E. U., Singh, S., Htet, T., Beauchamp, J., Ogbonna, N., et al. (2024) · Open Journal of Psychiatry · doi:10.4236/ojpsych.2024.145026 · Cited by 2 · see citing works
Evaluating AI-Powered Automation of Therapy Session Notes: A Pilot Randomized Controlled Trial
Batkhina, A. A. (2025) · Psychology · doi:10.4236/psych.2025.166044 · Cited by 2 · see citing works
VanHara, A., & Hage, D. S. (2025) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2571439 · Cited by 1 · see citing works
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“Users who have a positive attitude toward AI and feel confident in using technology are generally more receptive to AI-based features such as personalized product recommendations and easier search systems [35].”
Shankhwar, N., Ünal, A., & Singh, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26466-5.00012-x · Cited by 1 · see citing works
Xian, C., Yan, A., Wang, Y., Tsang, E. Y.-H., Huang, L., & Xu, D. J. (2026) · JMIR Formative Research · doi:10.2196/85138 · Cited by 1 · see citing works
“Most of the published work focuses on system development or clinical integration [8,9], and only a handful of studies have explored the clinical experience of care recipients.”
Nobile, B., Elyoseph, Z., Gourguechon-Buot, E., Guyodo, J., Garcia, J., Levkovich, I. I., Olié, É., Haber, Y., Levi‐Belz, Y., & Courtet, P. (2026) · JMIR Mental Health · doi:10.2196/88440 · Cited by 1 · see citing works
“In psychiatry, however, the integration of AI technologies has been slower than in other specialties, partly due to skepticism or reluctance among mental health professionals, particularly regarding their use in direct patient care [23].”
Clinicians’ Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review
Hudson, C., Phan, T. L., & Randall, M. (2026) · Journal of Medical Internet Research · doi:10.2196/92370 · Cited by 1 · see citing works
“…clinicians were cautiously optimistic about its potential to enhance and streamline patient assessment processes and support risk detection; however, more education, training, and guidelines around its usage were desired before adoption into mental health care could be considered [23,24,31,32,54].”
InfluentialSimoun, A., Fleet, A., Scharf, D. M., Pope, L. G., Spaeth‐Rublee, B., Goldman, M. L., & Pincus, H. A. (2024) · Translational Behavioral Medicine · doi:10.1093/tbm/ibae043 · Cited by 1 · see citing works
Wang, H.-W., Fong, C., Picq, D. C., & Tripathi, S. (2025) · Open Journal of Medical Psychology · doi:10.4236/ojmp.2025.144011 · Cited by 1 · see citing works
Yousafzai, A., Fatima, R., & Fatima, Z. (2025) · Research Square · doi:10.21203/rs.3.rs-7820094/v1 · Cited by 1 · see citing works
Goldie, J.-M. D., Dennis, S. J., Hipgrave, L., & Coleman, A. (2025) · doi:10.2196/preprints.71065
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Digital Maturity and AI: Ethical Considerations for Hospital Management
Buzila, E., & Rodríguez, F. G. (2025) · Springer eBooks · doi:10.1007/978-3-031-80704-6_19
Amalakanti, S., Avula, V. C. R., & Jillella, J. P. (2025) · Archives of Mental Health · doi:10.4103/amh.amh_35_25
Yan, A., Xian, C., Wang, Y., Tsang, E. Y.-H., Huang, L., & Xu, D. J. (2025) · doi:10.2196/preprints.85138
A Framework for Ethical Considerations in the Integration of AI and Systemic Therapy
Hessel, H. M., Hynes, K. C., He, Y., & Gizzi, S. (2025) · International Journal of Systemic Therapy · doi:10.1080/2692398x.2025.2587324
“It is an opportunity to maintain competency with technological advances and separate the field from those who may be more wary of the integration of technology (Zhang et al., 2023).”
Case studies in artificial intelligence–enabled patient counseling success stories
Patel, R. H., Agrawal, S., Rathod, D., & Prajapati, B. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43823-3.00004-2
Alotaibi, A., & Sas, C. (2026) · JMIR Mental Health · doi:10.2196/91884
Nurses’ perceptions of artificial intelligence use in clinical practice: a qualitative study
Karnjuš, I., & Žvanut, B. (2026) · Pielegniarstwo XXI wieku / Nursing in the 21st Century · doi:10.12923/pielxxiw-2026-0002 · Cited by 1 · see citing works
Noheda, S., Ramírez-Riveros, E., Rodriguez‐Moreno, S., Martín-Azañedo, C., Georgescu, A., & Roca, P. (2026) · doi:10.2196/preprints.93893
Sharma, S. K., Khan, A. R., Tejani, G. G., Bassir, D., & Tripathi, S. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001158
Evaluation Methods for AI in Public Health
Wu, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15872-7_4
Bakır, E., Torun, T., & Özdemir, H. (2026) · BMC Nursing · doi:10.1186/s12912-026-04717-6
“Research findings demonstrate that nurses’ perceptions and attitudes toward AI significantly impact the acceptance and effective application of new technologies [7, 8].”
Noheda, S., Ramírez, E. S., Rodriguez-Moreno, S., Martín-Azañedo, C., Georgescu, A., & Roca, P. (2026) · Journal of Medical Internet Research · doi:10.2196/93893 · Cited by 1 · see citing works
“These findings are also in line with previous research showing that mental health care professionals tend to recognize the practical utility of AI while simultaneously expressing concerns about trust, reliability, and appropriate use boundaries [7,8].”
Artificial intelligence and knowledge translation in healthcare: a scoping review
Glória, H. N. B., & Fragelli, T. B. O. (2026) · Ciência & Saúde Coletiva · doi:10.1590/1413-81232026315.23892025 · Cited by 1 · see citing works
Artificial intelligence and knowledge translation in healthcare: a scoping review
Glória, H. N. B., & Fragelli, T. B. O. (2026) · Ciência & Saúde Coletiva · doi:10.1590/1413-81232026315.23892025en
Clinician Perceptions of AI in Care: Cross-Sectional Survey
Stephenson, C., Eadie, J., Omrani, M., & Alavi, N. (2026) · JMIR Formative Research · doi:10.2196/95296
“Most respondents were undecided or unwilling to adopt AI, mirroring broader digital health trends where technological potential outpaces provider readiness, especially in relational care contexts [12,14,15].”
Gazi, V., Adamaki, A., & Papachristou, C. (2026) · AI and Ethics · doi:10.1007/s43681-026-01280-3
Choudhary, S., Kansal, A. R., Rana, P. S., & Mahala, P. (2026) · International Journal of Clinical Nursing Research Society · doi:10.5005/ijcnrs-11024-0019
Responsible AI in mental health services
Larrison, C. R., Sinha, G. R., & Kursuncu, U. (2026) · Social Work in Mental Health · doi:10.1080/15332985.2026.2730053
Nawaz, S., Nawaz, L., Ali, H., Yaqoob, M., & Saeed, M. M. (2026) · Digital Medicine · doi:10.1097/dm-2026-00028
Lutfi, S. L., Shidhani, O. A., Balushi, I. A., Hinai, K. A., Kalbani, U. A., Alawi, M. A., & Al-Busaidi, M. (2026) · doi:10.2196/preprints.111539
Friedrich, P., Obremski, D., Latoschik, M. E., & Wienrich, C. (2026) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3776574.3831112
- Authors
- Zhang, M., Scandiffio, J., Younus, S., Jeyakumar, T., Karsan, I., Charow, R., Salhia, M., & Wiljer, D.
- Affiliations
- University Health Network (CA)
- University of Toronto (CA)
- Corresponding author
- Wiljer, David
- ORCID
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-12-07
- Volume
- 7
- Pages
- e47847
- ISSN
- 2561-326X (ISSN-L)
- PMC
- PMC10739240
- OpenAlex
- W4389435613
- Cited by
- 67
- References
- 45
- Citations by year
- 2026: 30 · 2025: 29 · 2024: 8
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Formative Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Government of Canada
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence adoption, mental health services, mental health professionals, attitudes toward AI, artificial intelligence in education, staff training needs, organizational readiness, practice change, self-efficacy, humanized care, AI governance, knowledge translation, digital mental health tools, resistance to technology adoption, social workers, psychiatric nurses, psychiatrists, social work education, organizational change, staff buy-in
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