README.md

metadata

license: cc-by-nc-4.0
language:
  - en
tags:
  - medical
  - rheumatoid-arthritis
  - healthcare
  - diagnosis
pretty_name: Pre-screening Rheumatoid Arthritis Information Database
size_categories:
  - n<1K

PreRAID Dataset

Prescreening Rheumatoid Arthritis Information Database (PreRAID)
Developed by RespAI Lab at KIIT and KIMS Bhubaneswar.

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Overview

PreRAID is a structured dataset designed to evaluate the diagnostic capabilities of Large Language Models (LLMs) in Rheumatoid Arthritis (RA) diagnosis. This dataset provides real-world patient data, offering insights into RA prediction and reasoning accuracy.

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Data Description

• Total Records: 160 patient entries.
• Collection Location: KIMS Bhubaneswar, India.
• Demographic Breakdown:
  • Gender: 85% Female, 15% Male.
  • Diagnosis: 85% RA, 15% Non-RA.
• Languages Used: English and Odia.
• Data Collection: Through a structured online form supervised by RA medical professionals.

Key Information Captured

1. Demographic Details: Age, gender, language, and unique identifiers (e.g., KIMS ID).
2. Symptoms: Pain localization, onset duration, joint swelling, stiffness, and deformities.
3. Associated Conditions: Skin rashes, fever, ocular discomfort, and daily activity impacts.
4. Doctor-Verified Diagnoses: Ground truth and explanatory notes for RA and non-RA cases.

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Dataset Features

1. Structured Patient Records: Standardized text representation for uniform analysis.
2. Visual Aids: Diagrams for precise pain localization.
3. Embedded Vectors: Text embeddings for semantic relationships using GPT-4 text embedding models.
4. Storage: Organized in a vector database to enable retrieval-augmented generation (RAG).

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Research Insights

The dataset was utilized to investigate LLM misalignment in RA diagnosis. Key findings:

• LLMs achieved 95% accuracy in prediction but with 68% flawed reasoning.
• Misalignment between prediction accuracy and reasoning quality emphasizes the need for reliable explanations in clinical applications.

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Usage

The PreRAID dataset is ideal for:

1. Diagnostic Analysis: Evaluating AI model accuracy and reasoning quality for RA.
2. RAG Applications: Utilizing vectorized patient records for enhanced model reasoning.
3. Healthcare AI Research: Studying interpretability and trustworthiness of LLMs in medical settings.

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Citation

Please cite the following paper when using the PreRAID dataset:

@misc{maharana2025rightpredictionwrongreasoning,
      title={Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis}, 
      author={Umakanta Maharana and Sarthak Verma and Avarna Agarwal and Prakashini Mruthyunjaya and Dwarikanath Mahapatra and Sakir Ahmed and Murari Mandal},
      year={2025},
      eprint={2504.06581},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2504.06581}, 
}

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