Introduction
The Arabic Healthcare Chat Dataset is a rich collection of over 10,000 text-based conversations between customers and call center agents, focused on real-world healthcare interactions. Designed to reflect authentic language use and domain-specific dialogue patterns, this dataset supports the development of conversational AI, chatbots, and NLP models tailored for healthcare applications in Arabic-speaking regions.
Participant & Chat Overview
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Participants:
150+ native Arabic speakers from the FutureBeeAI Crowd Community
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Conversation Length:
300–700 words per chat
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Turns per Chat:
50–150 dialogue turns across both participants
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Chat Types:
Inbound and outbound
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Sentiment Coverage:
Positive, neutral, and negative outcomes included
Topic Diversity
The dataset captures a wide spectrum of healthcare-related chat scenarios, ensuring comprehensive coverage for training robust AI systems:
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Inbound Chats (Customer-Initiated):
Appointment scheduling, new patient registration, surgery and treatment consultations, diet and lifestyle discussions, insurance claim inquiries, lab result follow-ups
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Outbound Chats (Agent-Initiated):
Appointment reminders and confirmations, health and wellness program offers, test result notifications, preventive care and vaccination reminders, subscription renewals, risk assessment and eligibility follow-ups
This variety helps simulate realistic healthcare support workflows and patient-agent dynamics.
Language Diversity & Realism
This dataset reflects the natural flow of Arabic healthcare communication and includes:
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Authentic Naming Patterns:
Arabic personal names, clinic names, and brands
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Localized Contact Elements:
Addresses, emails, phone numbers, and clinic locations in regional Arabic formats
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Time & Currency References:
Use of dates, times, numeric expressions, and currency units aligned with Arabic-speaking regions
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Colloquial & Medical Expressions:
Local slang, informal speech, and common healthcare-related terminology
These elements ensure the dataset is contextually relevant and linguistically rich for real-world use cases.
Conversational Flow & Structure
Conversations range from simple inquiries to complex advisory sessions, including:
•Detailed problem-solving
•Treatment recommendations
•Support and feedback interactions
Each conversation typically includes these structural components:
•Greetings and verification
•Follow-up and feedback (where applicable)
This structured flow mirrors actual healthcare support conversations and is ideal for training advanced dialogue systems.
Data Format & Structure
Available in JSON, CSV, and TXT formats, each conversation includes:
•Full message history with clear speaker labels
•Metadata (e.g., topic tags, region, sentiment)
•Compatibility with common NLP and ML pipelines
Applications
This dataset supports a wide range of AI and NLP use cases in the healthcare sector:
•Healthcare Chatbots & Voice Assistants
•Appointment Scheduling Automation
•Sentiment and Emotion Detection
•NER and Medical Entity Extraction
•Text Classification & Intent Detection
•Predictive Response Models
•Arabic NLP Research in the Healthcare Domain
Ethical Collection & Data Security
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Consent-Based Contribution:
All participants provided informed consent
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Privacy Compliant:
No personally identifiable information is shared
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Secure Data Handling:
All data was collected and stored securely within FutureBeeAI's infrastructure
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Ethical Standards:
Adheres to best practices in AI ethics, healthcare data governance, and privacy protection
Dataset Expansion & Customization
The dataset is actively maintained and can be customized to meet specific needs:
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Custom Annotations:
Add NER tags, intent labels, sentiment scores, or medical category tags
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Topic Expansion:
Collect new chats for specific health areas (e.g., pediatrics, dermatology, telemedicine)
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Region-Specific Data:
Custom collection for different Arabic-speaking countries and dialects
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Multilingual Options:
Extend to additional languages or cross-lingual training needs
Licensing
This dataset is developed and owned by FutureBeeAI and is available under a commercial license. Flexible licensing terms are available for enterprise, startup, and academic use.
