The use of AI in mental health services to support decision-making: scoping review
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Abstract
Background: Recent advancements in artificial intelligence (AI) have changed the care processes in mental health, particularly in decision-making support for health care professionals and individuals with mental health problems. AI systems provide support in several domains of mental health, including early detection, diagnostics, treatment, and self-care. The use of AI systems in care flows faces several challenges in relation to decision-making support, stemming from technology, end-user, and organizational perspectives with the AI disruption of care processes. Objective: This study aims to explore the use of AI systems in mental health to support decision-making, focusing on 3 key areas: the characteristics of research on AI systems in mental health; the current applications, decisions, end users, and user flow of AI systems to support decision-making; and the evaluation of AI systems for the implementation of decision-making support, including elements influencing the long-term use. Methods: A scoping review of empirical evidence was conducted across 5 databases: PubMed, Scopus, PsycINFO, Web of Science, and CINAHL. The searches were restricted to peer-reviewed articles published in English after 2011. The initial screening at the title and abstract level was conducted by 2 reviewers, followed by full-text screening based on the inclusion criteria. Data were then charted and prepared for data analysis. Results: Of a total of 1217 articles, 12 (0.99%) met the inclusion criteria. These studies predominantly originated from high-income countries. The AI systems were used in health care, self-care, and hybrid care contexts, addressing a variety of mental health problems. Three types of AI systems were identified in terms of decision-making support: diagnostic and predictive AI, treatment selection AI, and self-help AI. The dynamics of the type of end-user interaction and system design were diverse in complexity for the integration and use of the AI systems to support decision-making in care processes. The evaluation of the use of AI systems highlighted several challenges impacting the implementation and functionality of the AI systems in care processes, including factors affecting accuracy, increase of demand, trustworthiness, patient-physician communication, and engagement with the AI systems. Conclusions: The design, development, and implementation of AI systems to support decision-making present substantial challenges for the sustainable use of this technology in care processes. The empirical evidence shows that the evaluation of the use of AI systems in mental health is still in its early stages, with need for more empirically focused research on real-world use. The key aspects requiring further investigation include the evaluation of the use of AI-supported decision-making from human-AI interaction and human-computer interaction perspectives, longitudinal implementation studies of AI systems in mental health to assess the use, and the integration of shared decision-making in AI systems.
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Beyond imaging, AI supports personalized oncology through predictive models that generate tailored treatment recommendations based on individual patient demographics, genomics, and clinical profiles.
“Beyond imaging, AI supports personalized oncology through predictive models that generate tailored treatment recommendations based on individual patient demographics, genomics, and clinical profiles [10].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [10]
The Introduction (Background) describes personalized AI-supported decision-making, and the Discussion (Principal Findings) identifies treatment-selection AI in mental health care. Neither passage supports the claim about oncology or recommendations based on patient demographics, genomics, and clinical profiles. (LLM, conf=0.86)
Experimental — accuracy not yet measuredBy integrating psychosocial considerations into clinical decision-making, AI holds promise for improving treatment efficacy and enhancing overall quality of life.
“By integrating psychosocial considerations into clinical decision-making, AI holds promise not only for improving treatment efficacy but also for enhancing overall quality of life [10].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [10]
The Introduction (Background; page unavailable) describes AI’s potential for personalized clinical decision-making, and the Results (Evaluation of Implementation and Use; page unavailable) describe its potential to support treatment selection and communication. Those passages do not link the integration of psychosocial considerations to improved treatment efficacy or overall quality of life; searches for those terms found no such account in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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- Authors
- Auf, H., Svedberg, P., Nygren, J., Nair, M., & Lundgren, L. E.
- Affiliations
- Halmstad University (SE)
- Corresponding author
- Auf, Hassan
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2025-01-24
- Volume
- 27
- Pages
- e63548
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC11806275
- OpenAlex
- W4404706159
- Cited by
- 56
- References
- 103
- Citations by year
- 2026: 39 · 2025: 17
- FWCI
- 1.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Högskolan i Halmstad
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, mental health services, clinical decision support, early detection of mental disorders, predictive AI, human-AI interaction, human-computer interaction, trust in AI, patient-physician communication, end-user involvement, hybrid care, self-care, process of care, AI evaluation, long-term use
- MeSH terms
- Artificial Intelligence, Mental Disorders, Decision Making, Humans, Mental Health Services, Decision Support Systems, Clinical
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