Update README.md

README.md

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 pretty_name: Pre-screening Rheumatoid Arthritis Information Database
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 - n<1K
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+---
+
+# 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}, 
+}
+
+---