Shifaa Arabic Medical Consultations 🏥📊

Overview 🌍

Shifaa is revolutionizing Arabic medical AI by addressing the critical gap in Arabic medical datasets. Our first contribution is the Shifaa Arabic Medical Consultations dataset, a comprehensive collection of 84,422 real-world medical consultations covering 16 Main Specializations and 585 Hierarchical Diagnoses.

🔍 Why is this dataset important?

• First large-scale Arabic medical dataset for AI applications.
• Highly structured and clean – no missing values, duplicates, or unnecessary columns.
• Hierarchical categorization of medical specialties for better NLP and AI training.
• Detailed responses (273 words on average) provide deep medical insights.

Dataset Details 📑

• Number of consultations: 84,422
• Main Specializations: 16
• Hierarchical Diagnoses: 585
• Average answer length: 273 words
• Languages: Arabic

Data Distribution 📊

The dataset covers a wide range of medical fields, making it suitable for various AI-driven applications such as:
✅ Medical Chatbots 🤖
✅ Predictive Models 🔮
✅ AI-powered Recommendation Systems 🏥
✅ Arabic Medical NLP Research 📖

Data Hierarchy 🌐

Our dataset follows a hierarchical structure to enhance precision and usability. Example for Obstetrics and Gynecology:

Word Cloud 🌟

This word cloud visualizes the most frequently used words in questions and answers related to muscle diseases.

Data Source 🔗

The dataset was collected from the Islam Web website using web-scraping tools.

Preprocessing & Cleaning 🛠️

✔ Removed duplicates and null values
✔ Corrected corrupted data
✔ Standardized hierarchical categories for consistency
✔ Eliminated hidden spaces and excess words

Usage & Applications 🚀

Shifaa Arabic Medical Consultations is designed for AI and NLP applications, making it ideal for:

• Medical Question Answering Systems
• Arabic Medical Chatbots
• AI-powered Medical Assistants
• Healthcare Research & AI-driven Text Analysis

How to Use 📥

Download the dataset from Hugging Face and start building your Arabic medical AI applications!

from datasets import load_dataset

dataset = load_dataset("Ahmed-Selem/Shifaa_Arabic_Medical_Consultations")