serhanayberkkilic/physiotherapy-evidence-qa

🏥 Physiotherapy Evidence QA: A Bilingual Clinical Corpus

Physiotherapy Evidence QA is a large-scale, expert-curated bilingual dataset comprising 143,711 aligned question-answer pairs. It focuses on evidence-based physiotherapy, musculoskeletal rehabilitation, outcome measures, and clinical research methodology.

This corpus is designed to facilitate the development of Medical Large Language Models (Med-LLMs), Clinical Decision Support Systems (CDSS), and Cross-Lingual Information Retrieval systems specifically for the physical therapy domain.

Principal Investigators / Curators:

• Serhan Ayberk KILIÇ (Senior AI Engineer)
• Fatma Betül DERDİYOK (PhD Student)
• Kasım SERBEST (Associate Professor)

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📖 Dataset Description

The dataset provides a structured taxonomy of clinical knowledge across musculoskeletal rehabilitation, physiotherapy interventions, and evidence-based treatment protocols. It bridges the gap between raw clinical literature and structured instruction-tuning data for medical LLM applications.

Data Generation Methodology:

• Question-answer pairs were generated using distillation techniques from approximately 3,000 scientific sources
• Sources include peer-reviewed articles, clinical textbooks, and academic theses
• All references were carefully selected by domain experts in physiotherapy and rehabilitation
• Each QA pair is traceable to its original source via source_file and source_page metadata

Key Features

• Bilingual Alignment: Every entry contains semantically aligned pairs in Turkish and English, enabling cross-lingual transfer learning.
• Granular Metadata: Each record is tagged with difficulty levels, question types (e.g., Diagnosis, Treatment, Etiology), and specific disease categories.
• Evidence-Based: Responses are derived from vetted clinical literature, ensuring high reliability for medical applications.

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🔬 Usage & Research Applications

This dataset is optimized for three primary LLM tasks in the healthcare domain:

Key Use Cases:

• Training domain-specific medical LLMs
• Building RAG systems for clinical decision support
• Developing bilingual medical AI assistants
• Benchmarking cross-lingual medical understanding

1. Domain-Specific Supervised Fine-Tuning (SFT)

Researchers can use this dataset to fine-tune general-purpose LLMs to improve their proficiency in physiotherapy terminology and clinical reasoning.

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("serhanayberkkilic/physiotherapy-evidence-qa")

# Example: Formatting data for Instruction Tuning (SFT)
def format_instruction(sample):
    return {
        "prompt": f"User: {sample['question_en']}\n\nAssistant: {sample['answer_en']}",
        "category": sample['disease_category_en']
    }

formatted_data = dataset['train'].map(format_instruction)
print(formatted_data[0]['prompt'])

2. Retrieval-Augmented Generation (RAG) Benchmarking

The dataset serves as a gold-standard knowledge base for testing RAG pipelines. The `source_file` and `source_page` columns allow researchers to verify citation accuracy and retrieval precision.

3. Cross-Lingual Medical Alignment

Given the parallel nature of the data (`question_tr` vs. `question_en`), this corpus is suitable for training translation models specialized in medical syntax or evaluating the zero-shot performance of English-centric models on Turkish medical queries.

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📊 Data Schema

The dataset is distributed in CSV format. The schema ensures full bilingual coverage for metadata:

Column Name	Type	Description
question_tr	String	Clinical inquiry in Turkish.
answer_tr	String	Evidence-based response in Turkish.
question_en	String	Clinical inquiry in English (Semantic translation).
answer_en	String	Evidence-based response in English.
disease_category_tr	Categorical	The specific pathology or topic in Turkish.
disease_category_en	Categorical	The specific pathology or topic in English (e.g., Lateral Epicondylitis).
question_type_tr	Categorical	Classification of the query in Turkish.
question_type_en	Categorical	Classification of the query in English (e.g., Anatomy, Diagnosis).
difficulty_tr	Ordinal	Difficulty level in Turkish (Kolay, Orta, Zor).
difficulty_en	Ordinal	Difficulty level in English (Easy, Medium, Hard).
keywords_tr	List	Domain-specific terminology in Turkish.
keywords_en	List	Domain-specific terminology in English.
source_file	String	The original academic reference document.
source_page	Integer	Page index in the source document for citation verification.

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📚 Citation

Use this BibTeX to cite the repository until the research paper is published:

@misc{kilic2025physiotherapyqa,
  author = {Kılıç, Serhan Ayberk and Derdiyok, Fatma Betül and Serbest, Kasım},
  title = {Physiotherapy Evidence QA: A Bilingual Clinical Corpus},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Repository},
  howpublished = {\url{https://huggingface.co/datasets/serhanayberkkilic/physiotherapy-evidence-qa}},
  note = {dataset}
}

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⚖️ License & Disclaimer

License: This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Disclaimer: While this dataset is curated by professionals, it is intended for research and educational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. AI models trained on this data should undergo rigorous safety testing before clinical deployment.