AXAI-CDSS: An Affective Explainable AI-Driven Clinical Decision Support System for Cannabis Use

As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiti...

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Bibliographic Details
Published in:2025 International Conference on Activity and Behavior Computing (ABC) pp. 1 - 14
Main Authors: Zhang, Tongze, Chung, Tammy, Dey, Anind, Bae, Sang Won
Format: Conference Proceeding
Language:English
Published: IEEE 21.04.2025
Subjects:
Affective Computing
Algorithmic Decisions
Artificial intelligence
Cannabis Intoxication
Cannabis Use Disorder
Cannabis-Intoxicated Behaviors
Causal Inference
Clinical Decision Support Systems (CDSS)
Decision support systems
Emotion recognition
Explainable AI
Explainable Artificial Intelligence (XAI)
Facial Emotion Recognition
Healthcare AI
Inference algorithms
Large language models
Large Language Models (LLMs)
Medical services
Passive Sensing
Personalized Intervention
Predictive models
Real-time systems
Sentiment analysis
Transparency
Trustworthy AI
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Summary:As cannabis use has increased in recent years, researchers have come to rely on sophisticated machine learning models to predict cannabis use behavior and its impact on health. However, many artificial intelligence (AI) models lack transparency and interpretability due to their opaque nature, limiting their trust and adoption in real-world medical applications, such as clinical decision support systems (CDSS). To address this issue, this paper enhances algorithm explainability underlying CDSS by integrating multiple Explainable Artificial Intelligence (XAI) methods and applying causal inference techniques to clarify the models' predictive decisions under various scenarios. By providing deeper interpretability of the XAI outputs using Large Language Models (LLMs), we provide users with more personalized and accessible insights to overcome the challenges posed by AI's "black box" nature. Our system dynamically adjusts feedback based on user queries and emotional states, combining text-based sentiment analysis with real-time facial emotion recognition to ensure responses are empathetic, contextadaptive, and user-centered. This approach bridges the gap between the learning demands of interpretability and the need for intuitive understanding, enabling non-technical users such as clinicians and clinical researchers to interact effectively with AI models. Ultimately, this approach improves usability, enhances perceived trustworthiness, and increases the impact of CDSS in healthcare applications.
DOI:10.1109/ABC64332.2025.11118599