MRI-MCQA#

Overview#

MRI-MCQA is a specialized benchmark composed of multiple-choice questions related to Magnetic Resonance Imaging (MRI). It evaluates AI models’ understanding of MRI physics, protocols, image acquisition, and clinical applications.

Task Description#

  • Task Type: Medical Imaging Knowledge Multiple-Choice QA

  • Input: MRI-related question with multiple answer choices

  • Output: Correct answer letter

  • Domain: Medical imaging, MRI physics, radiology

Key Features#

  • Specialized focus on MRI technology and applications

  • Tests understanding of MRI physics and protocols

  • Covers clinical MRI applications and sequences

  • Designed for evaluating medical imaging AI systems

  • Multiple-choice format for standardized evaluation

Evaluation Notes#

  • Default configuration uses 0-shot evaluation

  • Evaluates on test split

  • Simple accuracy metric

  • No training split available

Properties#

Property

Value

Benchmark Name

mri_mcqa

Dataset ID

extraordinarylab/mri-mcqa

Paper

N/A

Tags

Knowledge, MCQ, Medical

Metrics

acc

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Metric

Value

Total Samples

563

Prompt Length (Mean)

457.23 chars

Prompt Length (Min/Max)

259 / 888 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "84f179d7",
      "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D.\n\nWhich cardiac chambers are typically imaged on the short-axis view?\n\nA) RA and RV\nB) RA and LA\nC) LA and LV\nD) RV and LV"
    }
  ],
  "choices": [
    "RA and RV",
    "RA and LA",
    "LA and LV",
    "RV and LV"
  ],
  "target": "D",
  "id": 0,
  "group_id": 0,
  "metadata": {}
}

Prompt Template#

Prompt Template:

Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.

{question}

{choices}

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets mri_mcqa \
    --limit 10  # Remove this line for formal evaluation

Using Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['mri_mcqa'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)