Datasets:

OctoMed
/

NEJM-Image-Challenge

dataset_info:
  features:
    - name: image_id
      dtype: int64
    - name: date
      dtype: string
    - name: question
      dtype: string
    - name: relevant_context
      dtype: string
    - name: option_A_votes
      dtype: string
    - name: option_B_votes
      dtype: string
    - name: option_C_votes
      dtype: string
    - name: option_D_votes
      dtype: string
    - name: option_E_votes
      dtype: string
    - name: total_number_votes
      dtype: string
    - name: brier_score
      dtype: float64
    - name: image
      dtype: image
    - name: image_hash
      dtype: string
    - name: options
      sequence: string
    - name: answer
      dtype: string
  splits:
    - name: test
      num_bytes: 69947825
      num_examples: 947
  download_size: 69646346
  dataset_size: 69947825
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

NEJM - New England Journal of Medicine Image Challenge

Description

This dataset contains clinical cases from the NEJM Image Challenge series. Each case presents clinical images and patient information, requiring diagnosis selection from multiple options. We greatly appreciate and build from the original data source available at https://github.com/cx0/nejm-image-challenge. We modify the format slightly to have question, options, and answer fields as described below:

Data Fields

question: Clinical case description with patient presentation
options: Multiple choice diagnostic options
answer: The correct diagnosis
image: Clinical image(s) associated with the case

Splits

test: Test data for evaluation

Usage

from datasets import load_dataset

dataset = load_dataset("OctoMed/NEJM-Image-Challenge")

Citation

If you find our work helpful, feel free to give us a cite!

@article{ossowski2025octomed,
  title={OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning},
  author={Ossowski, Timothy and Zhang, Sheng and Liu, Qianchu and Qin, Guanghui and Tan, Reuben and Naumann, Tristan and Hu, Junjie and Poon, Hoifung},
  journal={arXiv preprint arXiv:2511.23269},
  year={2025}
}