Artificial Intelligence Painting Interactive Experience Discovers Possibilities for Emotional Healing in the Post-pandemic Era

Gao, Tanhao; Zhang, Dingwei; Hua, G. H.; Qiao, Yue; Zhou, Hongtao

doi:10.1007/978-3-031-35998-9_56

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Central to this domain is the capability of AI, particularly machine learning algorithms, to discern and replicate emotional cues in images. These algorithms, trained on extensive emotional datasets, enable AI to generate images that resonate with viewers, paralleling the emotional impact traditionally found in human-created art (Goodfellow et al, 2014 ; Sun et al, 2022 ; Gao et al, 2023 ). Projects like IBM's Watson and OpenAI's CLIP model illustrate AI's potential in creating emotionally engaging visual content (see Figure 1 ), utilizing advanced techniques to interpret and manipulate emotional content within imagery (Gatys et al, 2016 ; Radford et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Decoding emotional responses to AI-generated architectural imagery

Zhang,

Fort,

Giménez Mateu

2024

Front. Psychol.

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IntroductionThe integration of AI in architectural design represents a significant shift toward creating emotionally resonant spaces. This research investigates AI's ability to evoke specific emotional responses through architectural imagery and examines the impact of professional training on emotional interpretation.MethodsWe utilized Midjourney AI software to generate images based on direct and metaphorical prompts across two architectural settings: home interiors and museum exteriors. A survey was designed to capture participants' emotional responses to these images, employing a scale that rated their immediate emotional reaction. The study involved 789 university students, categorized into architecture majors (Group A) and non-architecture majors (Group B), to explore differences in emotional perception attributable to educational background.ResultsFindings revealed that AI is particularly effective in depicting joy, especially in interior settings. However, it struggles to accurately convey negative emotions, indicating a gap in AI's emotional range. Architecture students exhibited a greater sensitivity to emotional nuances in the images compared to non-architecture students, suggesting that architectural training enhances emotional discernment. Notably, the study observed minimal differences in the perception of emotions between direct and metaphorical prompts among architecture students, indicating a consistent emotional interpretation across prompt types.ConclusionAI holds significant promise in creating spaces that resonate on an emotional level, particularly in conveying positive emotions like joy. The study contributes to the understanding of AI's role in architectural design, emphasizing the importance of emotional intelligence in creating spaces that reflect human experiences. Future research should focus on expanding AI's emotional range and further exploring the impact of architectural training on emotional perception.

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Section: Literature Reviewmentioning

confidence: 99%

Decoding emotional responses to AI-generated architectural imagery

Zhang,

Fort,

Giménez Mateu

2024

Front. Psychol.

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Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review

Xian,

Chang,

Xiang

et al. 2024

Interact J Med Res

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Background Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain. Objective This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature. Methods Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques). Results In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care. Conclusions This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

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Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review (Preprint)

Xian,

Chang,

Xiang

et al. 2023

Preprint

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BACKGROUND Mental disorders have ranked among the top 10 prevalent causes of burden on a global scale. Generative artificial intelligence (GAI) has emerged as a promising and innovative technological advancement that has significant potential in the field of mental health care. Nevertheless, there is a scarcity of research dedicated to examining and understanding the application landscape of GAI within this domain. OBJECTIVE This review aims to inform the current state of GAI knowledge and identify its key uses in the mental health domain by consolidating relevant literature. METHODS Records were searched within 8 reputable sources including Web of Science, PubMed, IEEE Xplore, medRxiv, bioRxiv, Google Scholar, CNKI and Wanfang databases between 2013 and 2023. Our focus was on original, empirical research with either English or Chinese publications that use GAI technologies to benefit mental health. For an exhaustive search, we also checked the studies cited by relevant literature. Two reviewers were responsible for the data selection process, and all the extracted data were synthesized and summarized for brief and in-depth analyses depending on the GAI approaches used (traditional retrieval and rule-based techniques vs advanced GAI techniques). RESULTS In this review of 144 articles, 44 (30.6%) met the inclusion criteria for detailed analysis. Six key uses of advanced GAI emerged: mental disorder detection, counseling support, therapeutic application, clinical training, clinical decision-making support, and goal-driven optimization. Advanced GAI systems have been mainly focused on therapeutic applications (n=19, 43%) and counseling support (n=13, 30%), with clinical training being the least common. Most studies (n=28, 64%) focused broadly on mental health, while specific conditions such as anxiety (n=1, 2%), bipolar disorder (n=2, 5%), eating disorders (n=1, 2%), posttraumatic stress disorder (n=2, 5%), and schizophrenia (n=1, 2%) received limited attention. Despite prevalent use, the efficacy of ChatGPT in the detection of mental disorders remains insufficient. In addition, 100 articles on traditional GAI approaches were found, indicating diverse areas where advanced GAI could enhance mental health care. CONCLUSIONS This study provides a comprehensive overview of the use of GAI in mental health care, which serves as a valuable guide for future research, practical applications, and policy development in this domain. While GAI demonstrates promise in augmenting mental health care services, its inherent limitations emphasize its role as a supplementary tool rather than a replacement for trained mental health providers. A conscientious and ethical integration of GAI techniques is necessary, ensuring a balanced approach that maximizes benefits while mitigating potential challenges in mental health care practices.

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Artificial Intelligence Painting Interactive Experience Discovers Possibilities for Emotional Healing in the Post-pandemic Era

Cited by 3 publications

References 26 publications

Decoding emotional responses to AI-generated architectural imagery

Decoding emotional responses to AI-generated architectural imagery

Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review

Debate and Dilemmas Regarding Generative AI in Mental Health Care: Scoping Review (Preprint)

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