# ConVIScope: Visual Analytics for Exploring Patient Conversations

arXiv: https://arxiv.org/abs/2108.13514 · HTML: https://mlsanity.com/paper/2108.13514/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records](https://mlsanity.com/paper/1805.10724/) (2018)
- [Humane Visual AI: Telling the Stories Behind a Medical Condition](https://mlsanity.com/paper/2010.06296/) (2020)

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

- [StuGPTViz: A Visual Analytics Approach to Understand Student-ChatGPT Interactions](https://mlsanity.com/paper/2407.12423/) (2024)
- [JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets](https://mlsanity.com/paper/2407.03045/) (2024)

## Abstract (arXiv)

The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analytic system that tightly integrates interactive visualization with natural language processing in analyzing patient-doctor conversations. ConVIScope was developed in collaboration with healthcare professionals following a user-centered iterative design. Case studies with six domain experts suggest the potential utility of ConVIScope and reveal lessons for further developments.