---
dataset_info:
  features:
  - name: serial_number
    dtype: int64
  - name: task_id
    dtype: string
  - name: instruction
    dtype: string
  - name: image_sequence
    dtype: string
  - name: json_data
    dtype: string
  - name: num_steps
    dtype: int64
  - name: num_images
    dtype: int64
  - name: images
    list: image
  splits:
  - name: train
    num_bytes: 1678016377
    num_examples: 736
  - name: test
    num_bytes: 759410992
    num_examples: 315
  download_size: 2410022437
  dataset_size: 2437427369
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- image-text-to-text
tags:
- healthcare
- medical
- gui-automation
- vlm-agent
---

# CareFlow Benchmark

[**Project Page**](https://akashghosh.github.io/Care-Pilot/) | [**Paper**](https://huggingface.co/papers/2603.24157) | [**GitHub**](https://github.com/AkashGhosh/CarePilot)

**CareFlow** is a high-quality human-annotated benchmark for long-horizon software workflows across medical annotation tools, DICOM viewers, EHR systems, and laboratory information systems. It was introduced as part of the paper "CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare".

The benchmark is designed to evaluate vision-language models (VLMs) on complex, multi-step interactions in domain-specific medical contexts.

## Dataset Summary

CareFlow covers four major categories of clinical software:

| Category | Platforms |
|---|---|
| DICOM viewing & infrastructure | Orthanc, Weasis |
| Medical image computing & annotation | 3D Slicer |
| Hospital information & EMR systems | OpenEMR |
| Laboratory information systems | OpenHospital (OOD) |

### Dataset Statistics

| Split | Tasks | Avg. Steps | Min | Max | Actions |
|---|---|---|---|---|---|
| Train | 735 | 12.7 | 7 | 22 | 6 |
| Test | 315 | 12.9 | 9 | 24 | 6 |
| **Total** | **1050** | — | — | — | **6** |

### Action Space

The benchmark defines 6 primary atomic semantic actions:
- `CLICK`: Move the cursor and click at the specified item.
- `SCROLL`: Scroll the active view vertically or horizontally.
- `ZOOM`: Adjust the magnification level of the displayed image or view.
- `TEXT`: Type a string into the focused input field.
- `SEGMENT`: Create or edit a segmentation / ROI on the medical image.
- `COMPLETE`: Mark the workflow or task as finished.

## Usage

To run the CarePilot agentic pipeline on the CareFlow dataset, navigate to the `Agentic_Pipeline` directory in the [official repository](https://github.com/AkashGhosh/CarePilot) and use the following command:

```bash
python main.py --mode dataset --max_tasks 5
```

To generate Critic-augmented trajectories (SFT Data) from the training set:

```bash
python main.py --mode dataset --max_tasks 735 --start_task 0
```

## Citation

```bibtex
@inproceedings{ghosh2026carepilot,
  title={CarePilot: A Multi-Agent Framework for Long-Horizon Computer Task Automation in Healthcare},
  author={Akash Ghosh and Tajamul Ashraf and Rishu Kumar Singh and Numan Saeed and Sriparna Saha and Xiuying Chen and Salman Khan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026},
}
```