# PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets

arXiv: https://arxiv.org/abs/2407.16329 · HTML: https://mlsanity.com/paper/2407.16329/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Can Foundation Models Wrangle Your Data?](https://mlsanity.com/paper/2205.09911/) (2022)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics](https://mlsanity.com/paper/2304.01938/) (2023)
- [Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today](https://mlsanity.com/paper/2306.01499/) (2023)
- [Survey on Visual Analysis of Event Sequence Data](https://mlsanity.com/paper/2006.14291/) (2020)
- [LeafAI: query generator for clinical cohort discovery rivaling a human programmer](https://mlsanity.com/paper/2304.06203/) (2023)
- [Evaluation of AI Chatbots for Patient-Specific EHR Questions](https://mlsanity.com/paper/2306.02549/) (2023)
- [Tasks, Techniques, and Tools for Genomic Data Visualization](https://mlsanity.com/paper/1905.02853/) (2019)
- [Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions](https://mlsanity.com/paper/2009.00192/) (2020)

## Built on by (most cited first, 9 of 9)

- [ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent](https://mlsanity.com/paper/2507.18165/) (2025)
- [VizGenie: Toward Self-Refining, Domain-Aware Workflows for Next-Generation Scientific Visualization](https://mlsanity.com/paper/2507.21124/) (2025)
- [LLM-Assisted Visual Analytics: Opportunities and Challenges](https://mlsanity.com/paper/2409.02691/) (2024)
- [A Generative AI System for Biomedical Data Discovery with Grammar-Based Visualizations](https://mlsanity.com/paper/2509.16454/) (2025)
- [YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery](https://mlsanity.com/paper/2509.19182/) (2025)
- [Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting](https://mlsanity.com/paper/2603.07053/) (2026)
- [The Truth, the Whole Truth, and Nothing but the Truth: Automatic Visualization Evaluation from Reconstruction Quality](https://mlsanity.com/paper/2603.16873/) (2026)
- [Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy](https://mlsanity.com/paper/2606.03142/) (2026)
- [Multi-Agent Discovery and Resource-Aware Autonomous Exploration of Scientific Datasets](https://mlsanity.com/paper/2608.22045/) (2026)

## Abstract (arXiv)

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients. PhenoFlow pioneers an innovative workflow, where the LLM serves as a data wrangler while neurologists explore and supervise the output using visualizations and natural language interactions. This approach enables neurologists to focus more on decision-making with reduced cognitive load. To protect sensitive patient information, PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. This ensures that the results are both reproducible and interpretable while maintaining patient privacy. The system incorporates a slice-and-wrap design that employs temporal folding to create an overlaid circular visualization. Combined with a linear bar graph, this design aids in exploring meaningful patterns within irregularly measured BP data. Through case studies, PhenoFlow has demonstrated its capability to support iterative analysis of extensive clinical datasets, reducing cognitive load and enabling neurologists to make well-informed decisions. Grounded in long-term collaboration with domain experts, our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.