snuh/essential-level_medical_knowledge_dataset_sft

essential-levelmedicalknowledgedatasetsft

Dataset Description

• —Domain: 4 Medical Specialties (Obstetrics & Gynecology, Pediatrics/Neonatology, Emergency Medicine, Internal Medicine)
• —Language: Korean, English
• —Task: Clinical Reasoning, Question Answering
• —Augmentation Method: Evol-Instruct (Add Constraints only)
• —Source Data: AI-Hub Essential Medical Knowledge Data (필수의료 의학지식 데이터)

Dataset Summary

essential-level_medical_knowledge_dataset_sft is an integrated collection of augmented SFT data across 4 distinct medical domains, developed by the Healthcare AI Research Institute (HARI) at SNUH.

This dataset is derived and augmented from the [Essential Medical Knowledge Data (필수의료 의학지식 데이터)](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&&srchDataRealmCode=REALM006&aihubDataSe=data&dataSetSn=71875) provided by AI-Hub.

It focuses exclusively on complex clinical scenarios generated using the "Add Constraints" method from Evol-Instruct. Unlike simple fact-retrieval datasets, these examples force the model to navigate conflicting clinical limitations (e.g., comorbidities, resource constraints, contraindications). Each example includes a detailed Reasoning Path generated by a high-performance thinking model (qwen3-235b-a22b-thinking-2507), ensuring the model learns the logical process behind the diagnosis and treatment plan.

Note: This repository contains only the augmented data (44,985 samples) derived from the original AI-Hub source data.

Supported Tasks

• —Medical Question Answering: Answering complex clinical queries across various specialties.
• —Clinical Reasoning: Generating step-by-step logical explanations (<think> tags) before providing the final answer.
• —Domain Adaptation: Fine-tuning models for specific medical departments using the provided configs.

Dataset Structure

Data Instances

The dataset is provided in a JSONL format compatible with Chat templates. Each instance contains a conversation history where the assistant provides a hidden reasoning process within <think> tags before the final response.

{
  "messages": [
    {
      "role": "user",
      "content": "32세 여성은 임신 중기 이후 2회의 반복적 임신 손실 병력이 있으며... (중략) ... 이 환자에서 조기 분만 위험 감소를 위한 가장 적절한 치료 방침은 무엇인가?"
    },
    {
      "role": "assistant",
      "content": "<think>\n...(reasoning process)...\n</think>\n### **의료 문제 분석 및 단계별 추론 과정**\n...\n**정답: 3**..."
    }
  ]
}

Data Fields

• —messages: A list of dictionaries representing the conversation.
• —role: The role of the speaker (user or assistant).
• —content: The text content.
• —User Content: Contains the medical question with added constraints.
• —Assistant Content: Contains the Thinking Process (<think>...</think>) followed by the Final Answer.

Dataset Statistics

This repository covers 4 Medical Domains. You can load the entire dataset or specific domains using the config_name.

Usage

You can load the entire dataset or a specific domain using the config_name parameter.

from datasets import load_dataset

# 1. Load ALL data (Default)
dataset_all = load_dataset("snuh/essential-level_medical_knowledge_dataset_sft", "all")

# 2. Load Specific Domain (e.g., Obstetrics & Gynecology)
dataset_ob_gyn = load_dataset("snuh/essential-level_medical_knowledge_dataset_sft", "ob_gyn")

# 3. Load Another Domain (e.g., Internal Medicine)
dataset_im = load_dataset("snuh/essential-level_medical_knowledge_dataset_sft", "internal_medicine")

# Inspect an example
print(dataset_ob_gyn['train'][0]['messages'])

Dataset Creation

1. Source Data

The original question-answer pairs were sourced from the [Essential Medical Knowledge Data (필수의료 의학지식 데이터)](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&&srchDataRealmCode=REALM006&aihubDataSe=data&dataSetSn=71875) on AI-Hub. This dataset was selected for its high-quality, expert-verified medical content covering essential clinical domains.

2. Data Augmentation: Evol-Instruct (Add Constraints)

To bridge the gap between textbook knowledge and complex clinical reality, we applied the Evol-Instruct methodology, focusing exclusively on the "Add Constraints" technique.

Original simple questions were rewritten to include specific limitations that force the model to weigh conflicting factors:

• —Comorbidities: Adding conditions like heart failure, renal failure, or diabetes that complicate standard treatments.
• —Medication History: Introducing drug interactions (e.g., anticoagulants, immunosuppressants).
• —Environmental/Resource Constraints: Scenarios with limited equipment or lack of specialists.
• —Special Populations: Pregnancy, pediatric patients, elderly patients with multiple comorbidities.

3. Reasoning Path Generation

We utilized `qwen3-235b-a22b-thinking-2507` to generate high-quality reasoning paths for the augmented data. The generated reasoning follows a structured approach:

• —Constraint Satisfaction: Verifying if each option satisfies the specific constraints added during augmentation.
• —Hierarchical Elimination: Logically ruling out distractors based on priority and contraindications.

Limitations & Disclaimer

• —Synthetic Nature: The examples are synthetically generated to increase complexity. While they mimic real-world scenarios, they are artificial constructs.
• —AI-Generated Reasoning: The reasoning paths are generated by an AI model. Although they are designed to be logical and accurate, they may contain hallucinations or inaccuracies.
• —Not for Clinical Use: This dataset is intended for research and training purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment.

Citation

If you use this dataset, please cite the following:

@misc{essential_level_medical_knowledge_dataset_sft,
    title     = {essential-level_medical_knowledge_dataset_sft},
    url       = {https://huggingface.co/datasets/snuh/essential-level_medical_knowledge_dataset_sft},
    author    = {Healthcare AI Research Institute (HARI) of Seoul National University Hospital (SNUH)},
    month     = {January},
    year      = {2026}
}