**MediChaiD** is a conversational platform built on CHAD (Coordinated Heuristics Across Domains), a network of specialized AI agents designed to provide informational support, guidance, and continuous assistance along the healthcare journey. These agents do not deliver diagnoses instead, they help users understand their symptoms, ask the right questions, stay informed over time, and when needed, connect directly with qualified healthcare professionals.
- License: **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**
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## Description
**MediChaid** is an AI-based personal assistant designed to support and advise individuals diagnosed with Crohn's disease throughout the various stages of their condition. This project is a prototype aimed at exploring the potential applications of artificial intelligence within this specific area of healthcare.
The project is accompanied by a whitepaper: [MediChaiD an AI-Based Conversational Assistance for Chronic Disease Management](./MediChaiD%20an%20AI-Based%20Conversational%20Assistance%20for%20Chronic%20Disease%20Management.md) that provides a comprehensive overview of the design and development process, as well as the challenges and opportunities encountered along the way.
This Alpha implementation is intended to provide informative support and personalized recommendations by leveraging RAG augmented large language models (LLMs) to interact with the user. The alpha provided here is a prototype that serves as a proof of concept for the potential of AI in healthcare, specifically for patients with Crohn's disease.
This project is supported by a personal (non-peer-reviewed) research paper, which delves deeper into the topic, exploring the challenges and opportunities of developing an AI assistant for Crohn's disease patients. The paper provides broader context and discusses the design decisions made during the prototyping phase.
During the development of this project, I have also gatherd feedback from user testers, which has been invaluable source of information for future refining of s features and functionality. The feedback have been documented and discussed in the research paper.
