人工智能赋能艺术疗愈在超高龄老年慢性疼痛管理中的应用与评估研究

Abstract Background With the increasing integration of artificial intelligence (AI) into various aspects of daily life, there is a growing interest among designers and practitioners in incorporating AI into their fields. In health care domains like art therapy, AI is also becoming a subject of exploration. However, the use of AI in art therapy is still undergoing investigation, with its benefits and challenges being actively explored. Objective This study aims to investigate the integration of AI into art therapy practices to comprehend its potential impact on therapeutic processes and outcomes. Specifically, the focus is on understanding the perspectives of art therapists regarding the use of AI-assisted tools in their practice with clients, as demonstrated through the presentation of our prototype consisting of a deck of cards with words covering various categories alongside an AI-generated image. Methods Using a co-design approach, 10 art therapists affiliated with the American Art Therapy Association participated in this study. They engaged in individual interviews where they discussed their professional perspectives on integrating AI into their therapeutic approaches and evaluating the prototype. Qualitative analysis was conducted to derive themes and insights from these sessions. Results The study began in August 2023, with data collection involving 10 participants taking place in October 2023. Our qualitative findings provide a comprehensive evaluation of the impact of AI on facilitating therapeutic processes. The combination of a deck of cards and the use of an AI-generated tool demonstrated an enhancement in the quality and accessibility of therapy sessions. However, challenges such as credibility and privacy concerns were also identified. Conclusions The integration of AI into art therapy presents promising avenues for innovation and progress within the field. By gaining insights into the perspectives and experiences of art therapists, this study contributes knowledge for both practical application and further research.

… interface between artificial intelligence (AI) and chronic pain, … enhancing current treatments and yielding novel therapies. … -art architectures will be essential for improving chronic pain …

Background Digital health interventions can bridge barriers in access to treatment among individuals with chronic pain. Objective This study aimed to evaluate the perceived needs, engagement, and effectiveness of the mental health app Wysa with regard to mental health outcomes among real-world users who reported chronic pain and engaged with the app for support. Methods Real-world data from users (N=2194) who reported chronic pain and associated health conditions in their conversations with the mental health app were examined using a mixed methods retrospective observational study. An inductive thematic analysis was used to analyze the conversational data of users with chronic pain to assess perceived needs, along with comparative macro-analyses of conversational flows to capture engagement within the app. Additionally, the scores from a subset of users who completed a set of pre-post assessment questionnaires, namely Patient Health Questionnaire-9 (PHQ-9) (n=69) and Generalized Anxiety Disorder Assessment-7 (GAD-7) (n=57), were examined to evaluate the effectiveness of Wysa in providing support for mental health concerns among those managing chronic pain. Results The themes emerging from the conversations of users with chronic pain included health concerns, socioeconomic concerns, and pain management concerns. Findings from the quantitative analysis indicated that users with chronic pain showed significantly greater app engagement (P<.001) than users without chronic pain, with a large effect size (Vargha and Delaney A=0.76-0.80). Furthermore, users with pre-post assessments during the study period were found to have significant improvements in group means for both PHQ-9 and GAD-7 symptom scores, with a medium effect size (Cohen d=0.60-0.61). Conclusions The findings indicate that users look for tools that can help them address their concerns related to mental health, pain management, and sleep issues. The study findings also indicate the breadth of the needs of users with chronic pain and the lack of support structures, and suggest that Wysa can provide effective support to bridge the gap.

OBJECTIVE To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain. DESIGN Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores. METHODS Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12-weeks and compared to subjects' baseline. RESULTS Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks. CONCLUSION Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects' pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period.

… , particularly in chronic pain management, is an increasingly widely discussed and acknowledged integration of artificial intelligence into health care. Artificial intelligence-based …

This research presents an innovative smart community interactive art therapy platform that integrates multimodal computer graphics with resilient artificial intelligence adaptation mechanisms to address the growing challenges of home-based elderly care. The platform employs a four-layered hierarchical architecture encompassing perception, network, platform, and application layers to deliver personalized therapeutic interventions. The system utilizes multimodal data fusion algorithms to process visual, auditory, and haptic inputs while implementing adaptive learning mechanisms that continuously optimize user experiences based on individual preferences and capabilities. Experimental validation demonstrates superior performance with response times averaging 387 ms under 100 concurrent users, therapeutic recommendation accuracy of 87.3%, and user satisfaction scores of 4.2/5.0 across multiple evaluation dimensions. The resilient adaptation mechanisms achieved 99.7% service availability and 34% improvement in CPU utilization compared to conventional systems. Long-term usage tracking revealed sustained engagement patterns with minimal dropout rates over 6-month evaluation periods. The platform successfully addresses key limitations of traditional elderly care models by providing comprehensive support that encompasses cognitive stimulation, emotional well-being, and social connection while maintaining cost-effectiveness and scalability for large-scale deployment in smart community environments.

Abstract Approximate 50 million U.S. adults experience chronic pain. It is a widely held view that pain has been linked to sleep disturbance, mental problems, and reduced quality of life. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain can improve outcomes of patients and healthcare use. A comprehensive synthesis of the current use of AI-based interventions in pain management and pain assessment and their outcomes will guide the development of future clinical trials. This review aims to investigate the state of the science of AI-based interventions designed to improve pain management and pain assessment for adult patients. The electronic databases Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library were searched. The search identified 2131 studies, and 18 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess the quality. This review provides evidence that machine learning, deep learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment (44%), analyze self-reporting pain data (6%), predict pain (6%), and help physicians and patients to more effectively manage with chronic pain (44%). Findings from this review suggest that using AI-based interventions to improve pain recognition, pain prediction, and pain self-management is effective; however, most studies are pilot study which raises concerns about the generalizability of findings. Future research should focus on examining AI-based approaches on a larger cohort and over a longer period of time.

… , we developed an intervention using artificial intelligence (AI) to automatically adjust the CBT-CP session format based on patient feedback during their treatment course. We evaluated …