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---
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.
---
## 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.
---
## 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.
---
## 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).
---
## 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.
---
## 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.
---
## 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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