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

arXiv: https://arxiv.org/abs/2507.02920 · HTML: https://mlsanity.com/paper/2507.02920/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 70 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use](https://mlsanity.com/paper/1905.05134/) (2019)
- [TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations](https://mlsanity.com/paper/2207.04154/) (2022)
- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019)
- [CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis](https://mlsanity.com/paper/2001.05149/) (2020)
- [ViCE: Visual Counterfactual Explanations for Machine Learning Models](https://mlsanity.com/paper/2003.02428/) (2020)
- [A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?](https://mlsanity.com/paper/2304.04780/) (2023)
- [Levels of explainable artificial intelligence for human-aligned conversational explanations](https://mlsanity.com/paper/2107.03178/) (2021)
- [Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations](https://mlsanity.com/paper/2302.10671/) (2023)
- [LLMCheckup: Conversational Examination of Large Language Models via Interpretability Tools and Self-Explanations](https://mlsanity.com/paper/2401.12576/) (2024)
- [EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations](https://mlsanity.com/paper/2402.00491/) (2024)
- [Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology](https://mlsanity.com/paper/1712.06657/) (2017)
- [InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations](https://mlsanity.com/paper/2310.05592/) (2023)
- [Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare](https://mlsanity.com/paper/2211.16444/) (2022)
- [Explainable AI And Visual Reasoning: Insights From Radiology](https://mlsanity.com/paper/2304.03318/) (2023)
- [Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports](https://mlsanity.com/paper/2404.06162/) (2024)
- [Clinicians' Voice: Fundamental Considerations for XAI in Healthcare](https://mlsanity.com/paper/2411.04855/) (2024)
- [XEQ Scale for Evaluating XAI Experience Quality](https://mlsanity.com/paper/2407.10662/) (2024)

## Built on by (most cited first, 2 of 2)

- [Conversational AI-Enhanced Exploration System to Query Large-Scale Digitised Collections of Natural History Museums](https://mlsanity.com/paper/2603.10285/) (2026)
- [Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models](https://mlsanity.com/paper/2609.11860/) (2026)

## Abstract (arXiv)

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.