JMIR Publications

ABSTRACT

Background:

The increasing prevalence of cannabis use has motivated researchers to develop computational behavioral models that predict usage patterns and related health impacts in naturalistic environments. However, the opaque nature of many artificial intelligence (AI) systems limits clinicians’ ability to interpret outputs and undermines trust. Existing explainable AI (XAI) techniques often remain overly technical and do not account for the confusion or frustration clinicians may experience when interpreting complex model explanations.

Objective:

We propose and evaluate an Empathic Clinical Decision Support System (Empathic-CDSS) that integrates large language models (LLMs) with real-time emotion recognition and explainability modules. The goal is to provide transparent, adaptive, and emotionally attuned explanations that enhance interpretability, user confidence, and trust in AI-assisted clinical decision-making.

Methods:

Our Empathic-CDSS integrates XAI, causal inference, and affective computing within an interactive LLM-driven framework. Users' affective states were inferred using the circumplex model of affect, which characterizes emotions along two dimensions: valence (the degree of pleasure or displeasure) and arousal (the level of physiological activation or energy). Based on the captured emotional signals, the system dynamically adjusts the tone, structure, style, and complexity of its explanations generated by a fine-tuned LLM, enabling personalized and plain-language explanations. Thirty-three participants with diverse medical and technical backgrounds engaged with the system through guided evaluation tasks and post-session assessments to evaluate usability, interpretability, and trust.

Results:

Our Empathic-CDSS effectively generated personalized and transparent explanations that revealed the causal reasoning behind model predictions while enhancing users’ emotional engagement and trust in the system’s decision logic. Continuous, affect-based feedback enabled the system to dynamically adapt explanation delivery to individual user needs. Participants reported improved comprehension, perceived reliability, and satisfaction compared with a baseline CDSS system without emotion adaptation, demonstrating the feasibility and added value of integrating empathy–aware communication into AI-driven clinical decision support.

Conclusions:

This study introduces a transparent, trustworthy, and emotionally adaptive framework for AI-assisted prediction and clinical decision support. By uniting causal reasoning, affective sensing, and LLM-based natural-language explanations, the Empathic-CDSS offers a novel direction for developing emotionally intelligent and user-centered AI systems, with potential applications in behavioral monitoring and personalized interventions, including cannabis use and related health domains.