tutorAI | An AI medical Tutor

Project Overview

This project aims to develop an AI-powered medical tutor capable of generating original and authentic medical information. Think of it an AI medical ecyclopedia or text book with illustrations, citations (Researcher's style) and audio abilities, a perfect Lecturer or Tutor micmic. The AI tutor will provide educational content on various medical conditions and lessons, similar to those taught in medical school classes. It will also generate relevant images and cite sources accurately, ensuring the information is reliable and traceable.

Features

• Text Generation: Utilizes Llama3 and Sonnet 3.5 for generating detailed medical explanations.
• Image Generations:: Employs GANs to create high-quality medical illustrations and diagrams.
• Sequential Data Processing: Uses GRUs, RNNs and RAGs for handling sequential data and generating coherent responses.
• Source Citation: Automatically cites sources in APA, MLA and other formats to ensure information traceability.
• Interactive Learning: Includes quizzes, case studies, and interactive diagrams to enhance learning.

Technologies Used

• Pytorch and PyTorch Lightning: For modal training, evaluation, deployement and monitoring.
• Hugging face's Transformers: For advanced natural language processing tasks.
• Pine Vector database: for LLM data retrieval
• GraphQL: For API modelling and efficient data querying.
• PostgreSQL: for Database functions.
• Redis: For caching.
• Lightning.ai Studio: Development and Training environment.

Project version 1 under development