evalstone/evalscope/docs/en/benchmarks/medxpertqa.md

MedXpertQA

Overview

MedXpertQA is an expert-level medical multiple-choice benchmark designed to evaluate advanced medical knowledge and reasoning. It contains separate text-only and multimodal tracks built from challenging medical examination questions and reviewed by licensed physicians.

Task Description

• Task Type: Single-answer medical multiple choice
• Input: A clinical or biomedical question with answer choices, optionally accompanied by up to six images
• Output: One answer letter (A-J for Text or A-E for MM)
• Domain: Medicine across 17 specialties and 11 human body systems

Key Features

• The test split contains 4,450 questions: 2,450 Text questions with ten options and 2,000 MM questions with five options
• The MM track contains radiology, pathology, optical, photographic, diagram, chart, table, document, and vital-sign imagery
• Questions are annotated by medical task, body system, and question type; 3,307 test questions require reasoning and 1,143 assess understanding
• Questions underwent difficulty filtering, option augmentation, leakage mitigation, and multiple rounds of expert review

Evaluation Notes

• Primary metric: Accuracy by exact match of the predicted option letter
• The default prompt uses EvalScope's zero-shot chain-of-thought template, preserving the official step-by-step instruction and exact answer-letter scoring
• Set max_tokens high enough for the model to emit the required final ANSWER: [LETTER] line; truncated reasoning may otherwise fall back to the shared parser's last valid uppercase letter
• Results are reported separately for the Text and MM subsets and combined with sample-weighted aggregation
• MM images are stored in images.zip (about 517 MB) and read directly from the archive without extracting a second copy
• The published dataset has 4,460 records including ten development examples; this integration evaluates the 4,450 held-out test questions
• Paper | GitHub

Properties

Property	Value
Benchmark Name	medxpertqa
Dataset ID	evalscope/MedXpertQA
Paper	Paper
Tags	MCQ, Medical, MultiModal, Reasoning
Metrics	accuracy
Default Shots	0-shot
Evaluation Split	test

Data Statistics

Metric	Value
Total Samples	4,450
Prompt Length (Mean)	1135.22 chars
Prompt Length (Min/Max)	346 / 4771 chars

Per-Subset Statistics:

Subset	Samples	Prompt Mean	Prompt Min	Prompt Max
Text	2,450	1337.92	435	4771
MM	2,000	886.91	346	2335

Image Statistics:

Metric	Value
Total Images	2,852
Images per Sample	min: 1, max: 6, mean: 1.43
Resolution Range	323x34 - 4248x2144
Formats	jpeg, png

Sample Example

Subset: Text

{
  "input": [
    {
      "id": "3f1d2f2a",
      "content": "You are a helpful medical assistant."
    },
    {
      "id": "1a9f9143",
      "content": [
        {
          "text": "Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D,E,F,G,H,I,J. Think step by step before answering.\n\nWhich pat ... [TRUNCATED 885 chars] ... ere posterior wear undergoing shoulder arthroplasty\nI) 58-year-old male with glenoid retroversion of 12-degrees undergoing shoulder arthroplasty\nJ) 55-year-old male with glenoid retroversion of 8-degrees undergoing total shoulder arthroplasty"
        }
      ]
    }
  ],
  "choices": [
    "70-year-old male with glenoid retroversion of 18-degrees undergoing shoulder arthroplasty",
    "70-year-old female with humeral anteversion of 13-degrees undergoing shoulder arthroplasty",
    "63-year-old female with glenoid retroversion of 22-degrees and mild posterior wear undergoing shoulder arthroplasty",
    "65-year-old female with glenoid retroversion of 25-degrees undergoing shoulder arthroplasty",
    "65-year-old female with a glenoid retroversion of 13-degrees undergoing shoulder arthroplasty",
    "68-year-old female with glenoid retroversion of 20-degrees undergoing reverse shoulder arthroplasty",
    "72-year-old male with glenoid retroversion of 15-degrees undergoing shoulder arthroplasty",
    "65-year-old female with glenoid retroversion of 30-degrees and severe posterior wear undergoing shoulder arthroplasty",
    "58-year-old male with glenoid retroversion of 12-degrees undergoing shoulder arthroplasty",
    "55-year-old male with glenoid retroversion of 8-degrees undergoing total shoulder arthroplasty"
  ],
  "target": "E",
  "id": 0,
  "group_id": 0,
  "metadata": {
    "id": "Text-0",
    "medical_task": "Basic Science",
    "body_system": "Skeletal",
    "question_type": "Reasoning",
    "images": []
  }
}

Note: Some content was truncated for display.

Prompt Template

System Prompt:

You are a helpful medical assistant.

Prompt Template:

Answer the following multiple choice question. The last line of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}. Think step by step before answering.

{question}

{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets medxpertqa \
    --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=['medxpertqa'],
    dataset_args={
        'medxpertqa': {
            # subset_list: ['Text', 'MM']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)