AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy

Kim, Jiwon; Kang, Jung‐Woo; Kim, Taeeun; Song, Hayeon; Han, Jinyoung

doi:10.1145/3581641.3584087

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In this context, XAI is concerned with the development of methods that aim to enhance the models’ interpretability and facilitate operators’ comprehension. Kim et al [ 35 ] systematically summarized the application and problems associated with XAI technology in the domain of operator assistance systems, and provided valuable references to guide future research in the field of nuclear energy. Mortenson et al [ 36 ] conducted a comprehensive review of the application of AI in the nuclear power field, focusing on the analysis of research related to the concept of interpretability in AI systems.…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

Zhang

Xiao³

et al. 2023

Entropy

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Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.

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Section: Related Workmentioning

confidence: 99%

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

Zhang

Xiao³

et al. 2023

Entropy

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SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment

Kang,

Kim,

Yang

et al. 2024

Proceedings of the ACM Web Conference 2024

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Integrating Generative AI into Art Therapy: A Technical Showcase (Preprint)

Schmutz,

Kravchenko,

Ben Souissi

et al. 2024

Preprint

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BACKGROUND Mental health issues are prevalent and challenging both on a personal and societal level. Art therapy has been a recognized method of treatment, offering patients an expressive and non-verbal way to process emotions. With advancements in digital technologies, integrating artificial intelligence (AI) into therapeutic practices has the potential to enhance accessibility and effectiveness. However, the application of generative AI within art therapy remains underexplored, necessitating further investigation into its technical and ethical challenges. OBJECTIVE This study aims to develop and showcase a novel technical design that integrates generative AI into art therapy, specifically focusing on the use of text-to-image models. The goal is to provide a supportive tool that enhances patient expression and creative customization, while preserving the role of the therapist in guiding the therapeutic process. METHODS We define a simplified art therapy session process and demonstrate how it can be augmented with generative AI. Our technical implementation leverages edge detection, sketch-to-image models, and inpainting techniques to enable patients to refine their artwork through text prompts. We qualitatively evaluate the system using three illustrative examples: a digital sketch, a painted image, and a photograph of a sculpture. RESULTS The system successfully generated, refined and adapted versions of the exemplary artworks for all three cases, demonstrating its ability to maintain the essence of the original input. CONCLUSIONS The integration of generative AI into art therapy offers significant potential for enhancing accessibility and expressive possibilities for patients. While this proof-of-concept highlights technical feasibility, future research is required to assess therapeutic efficacy, user acceptance, and address ethical concerns, such as bias in AI-generated content. Our approach offers a foundation for further investigation into combining AI with traditional therapeutic practices, supporting the development of extended applications in clinical settings. Our implementations are publicly available at https://github.com/BFH-AMI/sds24.

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AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy

Cited by 3 publications

References 28 publications

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants

SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment

Integrating Generative AI into Art Therapy: A Technical Showcase (Preprint)

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