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seongsubae/KorMedMCQA-V

KorMedMCQA-V: A Multimodal Benchmark for Evaluating Vision-Language Models on the Korean Medical Licensing Examination KorMedMCQA-V is a multimodal multiple-choice question answering benchmark for evaluating vision-language models on the Korean Medical Licensing Examination. The dataset consists of 1,534 questions with 2,043 associated medical images from Korean Medical Licensing Examinations (2012-2023). Dataset Summary Total Questions: 1,534 Total Images: 2… See the full description on the dataset page: https://huggingface.co/datasets/seongsubae/KorMedMCQA-V.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face
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Dataset Card

KorMedMCQA-V: A Multimodal Benchmark for Evaluating Vision-Language Models on the Korean Medical Licensing Examination

![Paper](https://arxiv.org/abs/2602.13650) ![Dataset](https://huggingface.co/datasets/seongsubae/KorMedMCQA-V) ![Code](https://github.com/baeseongsu/kormedmcqa_v) ![Leaderboard](https://kormedmcqa-v.github.io/)

KorMedMCQA-V is a multimodal multiple-choice question answering benchmark for evaluating vision-language models on the Korean Medical Licensing Examination. The dataset consists of 1,534 questions with 2,043 associated medical images from Korean Medical Licensing Examinations (2012-2023).

Table of Contents

Dataset Summary

  • —Total Questions: 1,534
  • —Total Images: 2,043 (avg 1.33 images/question)
  • —Splits: test (2022-2023, 304 questions), test_full (2012-2023, 1,534 questions)
  • —Format: Parquet with base64-encoded images
  • —Image Modalities (9 categories): X-ray (586), Other (554), CT (336), ECG (164), Ultrasound (138), Endoscopy (122), NST (54), PBS (49), MRI (40)

Data Format

Each sample contains:

FieldTypeDescription
subjectstringSubject type (always "doctor")
yearint64Year of examination
periodint64Period of examination
q_numberint64Question number
questionstringQuestion text
A, B, C, D, EstringAnswer choices
answerstringCorrect answer (A-E)
imagesstringJSON string of base64-encoded image objects

Image Object Structure

images field is a JSON string containing an array of image objects with base64-encoded images:

json
[
  {
    "pic_num": "1",
    "modality": "XRAY",
    "image_base64": "data:image/png;base64,<base64>"
  }
]

The image_base64 field contains a full data URL.

Usage

Loading the Dataset

python
from datasets import load_dataset

dataset = load_dataset("seongsubae/KorMedMCQA-V", name="doctor", split="test_full")

for sample in dataset:
    print(f"Question: {sample['question']}")
    print(f"Answer: {sample['answer']}")

Viewing Images

python
import json
import base64
import io
from PIL import Image

sample = dataset[0]
images = json.loads(sample["images"])

for img in images:
    data_url = img["image_base64"]
    header, b64_str = data_url.split("base64,", 1)
    img_bytes = base64.b64decode(b64_str)
    pil_image = Image.open(io.BytesIO(img_bytes))
    pil_image.show()
    print(f"Modality: {img['modality']}, Size: {pil_image.size}")

Combining with KorMedMCQA

To evaluate on both text-only and image-dependent questions, combine the test split with sean0042/KorMedMCQA:

  • —KorMedMCQA (text-only) contains 2022-2024 data; filter to 2022-2023 for alignment
  • —KorMedMCQA-V (multimodal) contains 2022-2023 data
  • —Remove duplicate UID doctor-2022-2-64 to avoid double-counting
python
from datasets import load_dataset

# Load both datasets (test split = 2022-2023)
kormedmcqa = load_dataset("sean0042/KorMedMCQA", name="doctor", split="test")
kormedmcqa_v = load_dataset("seongsubae/KorMedMCQA-V", name="doctor", split="test")

# Filter KorMedMCQA for 2022-2023 and remove duplicate UIDs
allowed_years = [2022, 2023]
excluded_uids = ["doctor-2022-2-64"]

kormedmcqa_filtered = [
    s for s in kormedmcqa
    if s["year"] in allowed_years
    and f"{s['subject']}-{s['year']}-{s['period']}-{s['q_number']}" not in excluded_uids
]

print(f"Text-only: {len(kormedmcqa_filtered)}, Multimodal: {len(kormedmcqa_v)}")

For evaluation code, see the GitHub repository.

License

This dataset is licensed under CC BY-NC-SA 4.0.

Citation

bibtex
@dataset{kormedmcqa-v,
  title        = {KorMedMCQA-V: A Multimodal Benchmark for Evaluating Vision-Language Models on the Korean Medical Licensing Examination},
  author       = {Byungjin Choi and Seongsu Bae and Sunjun Kweon and Edward Choi},
  year         = {2025},
  publisher    = {HuggingFace},
  version      = {1.0},
 }

Contact

For questions or issues, please contact Byungjin Choi (choi328328@ajou.ac.kr) or Seongsu Bae (seongsu@kaist.ac.kr).