The role of AI-driven art therapy in supporting autism, mental health, and emotional well-being: An umbrella review
Bibliographic record
Abstract
Background: The integration of artificial intelligence (AI) into therapeutic practices has introduced new possibilities for enhancing mental health interventions. Among these innovations, AI-driven art therapy has emerged as a promising approach, combining the expressive and healing aspects of traditional art therapy with the adaptive and analytical strengths of AI. This modality shows particular potential in supporting individuals with autism spectrum disorder (ASD), mental health challenges, and those seeking emotional well-being. Aim: This umbrella review aimed to synthesize and evaluate existing evidence on the application, effectiveness, and implementation of AI-driven art therapy in improving outcomes for individuals with ASD, mental health conditions, and emotional distress. Methods: A comprehensive search was conducted across multiple databases-including PubMed, Scopus, Web of Science, PsycINFO, IEEE Xplore, and ACM Digital Library-to identify systematic and scoping reviews that adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The methodological quality of included reviews was assessed using the Joanna Briggs Institute (JBI) checklist. Thematic analysis was applied to extract and synthesize recurring patterns and core themes across studies. Results: The findings revealed significant therapeutic benefits of AI-driven art therapy, including enhanced communication and emotional regulation in individuals with ASD, and reduced symptoms of anxiety, depression, and Post-Traumatic Stress Disorder (PTSD) in mental health populations. Additionally, the platforms improved emotional well-being through personalized engagement, self-reflection, and increased autonomy. However, technological barriers, ethical concerns, and accessibility issues were noted as key limitations. Conclusions: AI-driven art therapy represents a transformative and accessible tool in modern therapeutic practices. While challenges remain, its potential to support diverse populations through individually tailored and engaging interventions makes it a valuable complement to traditional mental health care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".