Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916825404538880 |
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| author | Samimi, Reza Bhattacharya, Aditya Gosak, Lucija Stiglic, Gregor Verbert, Katrien |
| author_facet | Samimi, Reza Bhattacharya, Aditya Gosak, Lucija Stiglic, Gregor Verbert, Katrien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction Samimi, Reza Bhattacharya, Aditya Gosak, Lucija Stiglic, Gregor Verbert, Katrien Human-Computer Interaction Artificial Intelligence Machine Learning 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. |
| title | Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.02920 |