Abstract
This project aims to develop an Artificial Intelligence (AI) powered healthcare chatbot system designed to assist patients with medical inquiries, appointment scheduling, and health management advice. Utilizing natural language processing (NLP) and machine learning, the chatbot will provide accurate, timely, and personalized medical assistance, improving healthcare accessibility and patient engagement.
Introduction
AI-driven chatbots in healthcare are transforming patient care by providing a 24/7 interaction point. They handle routine inquiries, triage symptoms, and support chronic disease management. This project seeks to create a sophisticated AI healthcare chatbot that can understand and process user inputs naturally, provide medical information, guide users through symptoms checking, and integrate with healthcare provider databases for appointments and patient records management.
Existing System
Current healthcare chatbots often rely on rigid script-based interactions and limited AI capabilities, which can result in a suboptimal user experience and inaccuracies in handling complex medical queries. These systems may lack deep integration with healthcare systems and often do not support a personalized healthcare journey.
Proposed System
The proposed AI healthcare chatbot system will use advanced NLP models to understand and generate human-like responses. It will integrate with electronic health records (EHRs) to provide personalized advice and use machine learning to improve its response accuracy over time. The system will also feature a user-friendly interface accessible via web and mobile platforms.
Methodology
- Requirement Analysis: Identify specific user needs and healthcare provider requirements.
- Data Collection: Gather medical data, FAQs, and dialogue from healthcare sessions to train the chatbot.
- System Design: Architect the chatbot with modular components for NLP, user management, and API integrations.
- Chatbot Training: Utilize frameworks like Rasa or Dialogflow to train the chatbot on medical data, ensuring it understands and processes medical terminology effectively.
- Integration: Connect the chatbot with backend systems for EHR access and appointment scheduling.
- Testing and Iteration: Conduct rigorous testing with real users and iterate based on feedback to ensure reliability and user satisfaction.
- Deployment and Monitoring: Deploy the chatbot on cloud platforms and monitor its performance, making adjustments as needed for scalability and security.
Technologies Used
- Python: For backend development and machine learning tasks.
- Rasa/Dialogflow: For building and training the chatbot.
- TensorFlow/PyTorch: For custom NLP model enhancements.
- React: For developing a user-friendly front-end interface.
- Firebase/Amazon Web Services: For hosting, database management, and API integration.
- HL7 FHIR: For healthcare data interoperability standards.
