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 | Paper | GitHub
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 and use the following command:
python main.py --mode dataset --max_tasks 5
To generate Critic-augmented trajectories (SFT Data) from the training set:
python main.py --mode dataset --max_tasks 735 --start_task 0
Citation
@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},
}