In the field of graphic design, the application of Artificial Intelligence (AI) is reshaping the design process. This study employs the Critical Interpretive Synthesis (CIS) approach to explore the impacts and challenges of AI on graphic design. Through a comprehensive review of 33 papers, this research reveals four research paradigms of AI in graphic design: Artificial Intelligence Driven Design Automation and Generation (AIDAG), Artificial Intelligence Assisted Graphic Design and Image Processing (AGDIP), Artificial Intelligence in Art and Creative Design Processes (AACDP), and Artificial Intelligence Enhanced Visual Attention and Emotional Response Modeling (AVERM). These paradigms demonstrate the multidimensional role of AI in design, ranging from automation to emotional interaction. The findings suggest that AI serves a dual role as both a design tool and a medium for innovation. AI not only enhances the automation and efficiency of the design process but also fosters designers' creative thinking and understanding of users' emotional needs. This study also proposes a path for the application of the four paradigms in the graphic design process, providing effective design ideas for future design workflows.

Background: The World Health Organization reports that >1 billion people worldwide experience mental disorders, with the prevalence of depression and anxiety among children and adolescents at 2.6% and 6.5%, respectively. However, commonly used clinical questionnaires such as the Hamilton Depression Rating Scale and the Beck Depression Inventory suffer from several problems, including the high degree of overlap of symptoms of depression with those of other psychiatric disorders and a lack of professional supervision during administration of the questionnaires, which often lead to inaccurate diagnoses. In the wake of the COVID-19 pandemic, the health care system is facing the dual challenges of a surge in patient numbers and the complexity of mental health issues. Artificial Intelligence (AI) technology has now been shown to have great promise in improving diagnostic accuracy, assisting clinical decision-making, and simplifying questionnaire development and data analysis. Objective: This review aimed to explore the current applications, potential benefits, and issues of AI in medical questionnaires, focusing on its role in 3 main functions: assessment, development, and prediction. The global mental health burden remains severe. Methods: The review included peer-reviewed studies that applied AI technologies to medical, psychological, or physiological questionnaires and reported measurable outcomes; non–peer-reviewed, non-English/Chinese, ethically noncompliant, or AI-unrelated studies were excluded. Five databases (PubMed, Embase, Cochrane Library, Web of Science, and CNKI) were searched from inception through September 2024. Three independent reviewers conducted data extraction, quality appraisal using the Joanna Briggs Institute tools, and narrative synthesis of AI applications across questionnaire assessment, development, and prediction tasks. Results: Of 49,091 publications, a total of 14 (0.03%) studies met the inclusion criteria. AI technologies showed advantages in assessment, such as distinguishing myalgic encephalomyelitis or chronic fatigue syndrome from long COVID-19 with 92.18% accuracy. In questionnaire development, natural language processing using generative models such as ChatGPT was used to construct culturally competent scales. In terms of disease prediction, one study had an area under the curve of 0.790 for cataract surgery risk prediction. Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. Despite the positive findings, only 21% (3/14) of the studies had entered the clinical validation phase, whereas the remaining 79% (11/14) were still in the exploratory phase of research. Most of the studies (10/14, 71%) were rated as being of moderate methodological quality, with major limitations including lack of a control group, incomplete follow-up data, and inadequate validation systems. Conclusions: In summary, the integrated application of AI in medical questionnaires has significant potential to improve diagnostic efficiency, accelerate scale development, and promote early intervention. Future research should pay more attention to model interpretability, system compatibility, validation standardization, and ethical governance to effectively address key challenges such as data privacy, clinical integration, and transparency.

This study aims to explore how cultural identity influences the protection and innovation of traditional crafts through multidimensional pathways. Using the examples of macau shipbuilding and portuguese tile painting, 14 influencing factors were extracted from three dimensions—culture and expression, aesthetics and creation, and cognition and emotion—through the Delphi method and expert feedback. The decision-making trial and evaluation laboratory (DEMATEL) method was used to analyze causal relationships, and interpretive structural modelling (ISM) was employed to construct a hierarchical model consisting of core, direct, and indirect factors. The results indicate that historical continuity, knowledge transmission, and the integration of tradition and modernity are core factors, forming the cultural foundation for traditional craft innovation. Direct factors, such as creative expression, local culture, and emotional communication, play a pivotal role in connecting the core and indirect layers, while indirect factors like visual appeal, design originality, and craft education reflect the multidimensional value of traditional crafts. The study provides a clear, hierarchical pathway that explains the systematic process from cultural identity to innovation practice, offering valuable insights for sustainable craft innovation in the context of globalization.

This randomized controlled trial evaluated the efficacy ofAI-assisted drawing therapy for children with ADHD, comparing its impact on core symptoms and functional impairments against traditional drawing interventions while assessing engagement and therapeuticoutcomes. Forty-one children (ages 7-10) with ADHD were randomized into AI-assisted (n=19) or traditional drawing therapy (n=22) groups (Trial registered in ChiCTR: ChiCTR2400089640). The AI intervention employed a process that captured children's drawings, converted them into keywords, generated AI responses based on those keywords, and provided feedback to the children, while the control group received therapist-guided verbal feedback. SNAP-IV-26(Swanson, Nolan and Pelham-IV rating scales) and WFIRS-P(Weiss Functional Impairment Rating Scales-Parent) scales assessed at baseline (T1) and post-intervention (T2) at a 24-week interval, with data analyzed using independent t-tests (p<0.05). Both AI-assisted and traditional drawing therapies significantly reduced core ADHD symptoms. In the before-and-after comparison within the group, the AI group showed targeted improvements in attention (p = 0.039) and oppositional defiant behaviour (p = 0.025), while traditional therapy produced broader symptom reductions across all domains (p < 0.05). Between-group comparisons showed that the AI-assisted group achieved greater improvement in inattention (Δ = -1.09, p = 0.035), whereas the traditional drawing group improved more on oppositional defiant behavior (Δ=+1.706, p= 0.010) and the SNAP-IV total score (Δ=+4.777, p = 0.041). On the WFIRS-P, the AI group showed advantages for the Family domain (p = 0.041) and the total score (p = 0.047), while Self-Concept favored the traditional drawing group (p = 0.036). AI-assisted, drawing-based art therapy produced greater improvement in the pre-specified primary outcome (SNAP-IV inattention) and select WFIRS-P domains (e.g., Family, total score), whereas conventional drawing therapy yielded broader reductions in oppositional defiant symptoms and the overall SNAP-IV severity. Effects were modest and domain-specific; larger, multi-center trials are needed to confirm efficacy and identify which children benefit most.

Background: The efficacy of art therapy as an adjuvant treatment for schizophrenia remains inconclusive, and variation in the effects of art therapy on schizophrenia by the type of schizophrenia, severity of schizophrenia, type of art therapy, follow-up duration, or different populations has never been systematically assessed. The objective of this study is to systematically evaluate the effects of art therapy on schizophrenia and to determine whether there are some potential influencing factors affecting the effects of art therapy. Methods: Seven online databases will be searched from their inception until June 30, 2022. All the relevant randomized clinical trials (RCTs) comparing art therapy plus standardized treatment versus standardized treatment alone for schizophrenia will be selected and assessed for inclusion. The Cochrane risk-of-bias tool will be used to evaluate the methodological quality of the included RCTs. Review Manager 5.4 will be used to analyze all the data obtained. Mental health symptoms are defined as the primary outcome, and the secondary outcomes include the Global Assessment of Functioning score, quality of life, functional remission, and the level of self-esteem. Subgroup analyses will be performed based on the type of schizophrenia, severity of schizophrenia, type of art therapy, follow-up duration, or different populations. Results: The results will be published in a peer-reviewed journal. Conclusions: This updated systematic review and subgroup meta-analysis will evaluate the effects of art therapy as adjunctive treatment to standardized treatment in patients with schizophrenia and determine whether there are some potential confounding variables affecting the effects of art therapy on the outcomes of schizophrenia patients, thus strengthening the evidence base for the clinical application of this combination therapy for schizophrenia.