Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction

• URL: http://arxiv.org/abs/2507.02920v1
• Date: Wed, 25 Jun 2025 14:56:20 GMT
• Title: Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
• Authors: Reza Samimi, Aditya Bhattacharya, Lucija Gosak, Gregor Stiglic, Katrien Verbert,
• Abstract summary: We present an integrated decision support system that combines interactive visualizations with a conversational agent to explain diabetes risk assessments.<n>We conducted a mixed-methods study with 30 healthcare professionals and found that the conversational interactions helped healthcare professionals build a clear understanding of model assessments.
• Score: 1.8538021146309331
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Healthcare professionals need effective ways to use, understand, and validate AI-driven clinical decision support systems. Existing systems face two key limitations: complex visualizations and a lack of grounding in scientific evidence. We present an integrated decision support system that combines interactive visualizations with a conversational agent to explain diabetes risk assessments. We propose a hybrid prompt handling approach combining fine-tuned language models for analytical queries with general Large Language Models (LLMs) for broader medical questions, a methodology for grounding AI explanations in scientific evidence, and a feature range analysis technique to support deeper understanding of feature contributions. We conducted a mixed-methods study with 30 healthcare professionals and found that the conversational interactions helped healthcare professionals build a clear understanding of model assessments, while the integration of scientific evidence calibrated trust in the system's decisions. Most participants reported that the system supported both patient risk evaluation and recommendation.

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