Background The rapid integration of digital technologies into art therapy reflects both technological advances and the growing need for flexible, accessible mental health care. Objective This scoping review aimed to systematically map and synthesize empirical research in English on technology-integrated art therapy published from 2016 to August 2026. Methods A comprehensive search across 9 electronic sources yielded 697 records, of which 47 empirical studies met the inclusion criteria according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Results The included studies were methodologically heterogeneous, comprising qualitative (n=19, 40.4%), quantitative (n=12, 25.5%), and mixed methods (n=16, 34%) designs, and were largely characterized by small samples and exploratory or feasibility-oriented approaches. Among the quantitative and mixed methods studies, 9 used randomized controlled or randomized experimental designs, while the remainder used nonrandomized or other designs. Reported quantitative outcomes included improvements in cognitive functioning, emotional regulation, mental health, behavioral symptoms, and functional outcomes; however, evidence for clinical effectiveness remained limited and heterogeneous. Qualitative and mixed methods studies reported potential benefits related to engagement, reduced performance-related anxiety, agency, emotional expression, and creative exploration. Digital affordances such as editing, layering, and multimodal creation were associated with iterative meaning-making and expanded expressive possibilities. In immersive contexts, particularly virtual reality, participants described altered sensory and embodied experiences. At the same time, technology raises concerns regarding privacy, data security, boundaries and setting, and the potential impact on the therapeutic relationship. Conclusions Technology-integrated art therapy appears to offer promising possibilities for expanding access, engagement, expression, and therapeutic experience, but the current evidence remains too limited and heterogeneous to establish clinical effectiveness or mechanisms of change.
Generative AI chatbots are increasingly used for mental health–related purposes, yet little is known about the factors associated with such use or AI chatbot dependence. This study examined factors associated with young adults’ use of AI chatbots for emotional self-disclosure and mental health–related support, as well as the associations between AI chatbot use and dependence. A cross-sectional online survey of individuals aged 18 to 29 years was conducted in Japan. Self-report measures assessed psychosocial factors (self-esteem and loneliness), mental health symptoms (anxiety and depression), AI chatbot use (emotional self-disclosure and mental health–related support), and AI chatbot dependence (emotional dependence and social anxiety–related dependence). Of 820 participants, 381 (46.5%) reported using AI chatbots for emotional self-disclosure at least once in the past month, including 65 (7.9%) who reported doing so every day or almost every day. In addition, 336 (41%) reported using AI chatbots for mental health–related support, including 53 (6.5%) who reported doing so every day or almost every day. Anxiety and depressive symptoms were combined into a composite measure of mental health symptoms because they are highly correlated. Multivariable logistic regression showed that greater mental health symptom severity, but not self-esteem or loneliness, was associated with both emotional self-disclosure (odds ratio [OR] 2.23, 95% CI 1.85-2.69; P<.001) and mental health–related support (OR 2.42, 95% CI 2.01-2.94; P<.001). Among emotional self-disclosure users (n=381), the frequency of emotional self-disclosure was positively associated with emotional dependence (β=0.334; P<.001) and social anxiety–related dependence (β=0.307; P<.001). Path analyses indicated that a model specifying psychosocial factors and mental health symptoms as antecedent variables to AI chatbot use and dependence fit the data better than an alternative model specifying AI chatbot use and dependence as the antecedent variables (comparative fit index [CFI]=0.994 vs 0.583; root mean square error of approximation [RMSEA]=0.033 vs 0.293). In this model, lower self-esteem and greater loneliness were associated with more severe mental health symptoms, which, in turn, were associated with greater AI chatbot dependence, primarily through direct pathways and, to a lesser extent, through more frequent emotional self-disclosure. Among emotional self-disclosure users, loneliness was the only significant psychosocial correlate of emotional dependence (F1,369=7.78; P=.006). Emotional engagement with AI chatbots was common in this sample of young adults in Japan. Findings suggest that young adults experiencing mental health symptoms may be more likely to use AI chatbots for emotional self-disclosure and mental health–related support, while loneliness may be particularly relevant to emotional dependence among users. These findings highlight the need to understand AI chatbot use not only as a technological behavior but also as part of young adults’ broader emotional and help-seeking landscape.
BackgroundPsychiatric diagnostic practice faces persistent challenges in adequately capturing the complexity, heterogeneity, and dynamic nature of mental disorders. Conventional categorical classification systems such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases, 11th Revision (ICD-11) rely on rigid symptom-based criteria that often fail to represent multifactorial etiologies, dimensional symptom profiles, and high rates of comorbidity. Although dimensional models, domain-specific ontologies, and alternative taxonomies have been developed to address these limitations, they are typically applied in isolation, resulting in fragmented knowledge representations and limited interoperability. This lack of semantic integration hampers data harmonization and cross-domain analysis. ObjectiveThe objective of this work is to develop and present the Meta Mesh Ontology (MMO) and its Core Domain Framework (CDF) as an integrative approach for mapping heterogeneous psychiatric knowledge bases and evaluating ontology structure, logical consistency, and specified retrieval and inference behavior. MethodsWe designed a modular ontology architecture grounded in the Basic Formal Ontology to ensure structural and semantic consistency across biological, psychological, and social domains. Existing psychiatric frameworks and ontologies—including dimensional models, diagnostic taxonomies, and socioenvironmental knowledge systems—were systematically analyzed and represented through domain modules using a documented alignment and mapping procedure. The MMO was implemented using formal semantic web standards, including the Web Ontology Language and the Resource Description Framework (RDF), and aligned with established biomedical ontologies and informatics standards to support explicit mappings and modular extensions across domains. Evaluation used 2 MMO and CDF metric sets and 10 module metric sets to quantify ontology size, semantic alignments, external integration, and dataset readiness. We also assessed Web Ontology Language 2 Description Logic (OWL 2 DL) conformance, logical consistency, and 11 competency questions (including 1 entailment test) and 4 negative controls, in an outpatient scenario with 50 variables. ResultsThe MMO provides a shared semantic framework for representing links among heterogeneous psychiatric knowledge sources. Its modular design allows domain-specific subontologies to be incorporated under a common architecture. MMO and CDF comprised 2238 triples, 208 classes, 28 properties, 3 individuals, and 103 alignments (100 external) across 5 frameworks. The 10 modules added 967 triples and 171 alignments (116 external), with 21 integrations and complete label coverage (54/54 resources). The ontology resources conformed to OWL 2 DL; HermiT (Information Systems Group) detected no logical inconsistency, and all 11 competency questions and 4 negative controls produced the expected results. All 50 variables were linked to at least 1 CDF anchor, and 47 had an explicit Simple Knowledge Organization System (SKOS) mapping. ConclusionsThe MMO provides a modular foundation for representing links among diverse psychiatric knowledge systems and data modalities. The current evaluation characterizes its structure, mappings, and behavior in the specified tests. Further research should assess mapping validity, governed clinical data, operational performance, and clinical utility.
