metadata

license: cc-by-4.0
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - medical
  - multimodal
  - in-context-learning
  - vqa
  - benchmark
dataset_info:
  features:
    - name: question
      dtype: string
    - name: answer
      dtype: string
    - name: image
      dtype: image
    - name: image_url
      dtype: string
    - name: problem_id
      dtype: string
    - name: order
      dtype: int64
    - name: parquet_path
      dtype: string
    - name: speciality
      dtype: string
    - name: flag_answer_format
      dtype: string
    - name: flag_image_type
      dtype: string
    - name: flag_cognitive_process
      dtype: string
    - name: flag_rarity
      dtype: string
    - name: flag_difficulty_llms
      dtype: string
  splits:
    - name: train
      num_bytes: 94510405
      num_examples: 517
  download_size: 90895608
  dataset_size: 94510405
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning

Paper | Project page | Code

Introduction

Multimodal in-context learning (ICL) remains underexplored despite the profound potential it could have in complex application domains such as medicine. Clinicians routinely face a long tail of tasks which they need to learn to solve from few examples, such as considering few relevant previous cases or few differential diagnoses. While MLLMs have shown impressive advances in medical visual question answering (VQA) or multi-turn chatting, their ability to learn multimodal tasks from context is largely unknown.

We introduce SMMILE (Stanford Multimodal Medical In-context Learning Evaluation), the first multimodal medical ICL benchmark. A set of clinical experts curated ICL problems to scrutinize MLLM's ability to learn multimodal tasks at inference time from context.

Dataset Access

The SMMILE dataset is available on HuggingFace:

from datasets import load_dataset
load_dataset('smmile/SMMILE', token=YOUR_HF_TOKEN)
load_dataset('smmile/SMMILE-plusplus', token=YOUR_HF_TOKEN)

Note: You need to set your HuggingFace token as an environment variable:

export HF_TOKEN=your_token_here

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Citation

If you find our dataset useful for your research, please cite the following paper:

@article{rieff2025smmile,
      title={SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning},
      author={Melanie Rieff and Maya Varma and Ossian Rabow and Subathra Adithan and Julie Kim and Ken Chang and Hannah Lee and Nidhi Rohatgi and Christian Bluethgen and Mohamed S. Muneer and Jean-Benoit Delbrouck and Michael Moor},
      year={2025},
      eprint={2506.21355},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2506.21355},
}

Acknowledgments

We thank the clinical experts who contributed to curating the benchmark dataset.