Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
Journal:
arXiv
Published Date:
Jun 25, 2025
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.
