Minal Ali@MinalAli

Access to affordable healthcare remains a critical challenge, especially for financially disadvantaged patients. Administrative inefficiencies, lack of transparency in treatment costs, and unequal allocation of donated funds exacerbate the problem. Existing systems often fail to prioritize those in dire need, leaving many patients without timely access to necessary treatments. There is an urgent need for a system that leverages AI to ensure fairness, transparency, and efficiency in healthcare administration and donation allocation. CareAI aims to tackle the critical issue of access to affordable healthcare, especially for financially disadvantaged patients, by addressing the inefficiencies in healthcare administration and donation allocation. The solution combines the power of Artificial Intelligence (AI) with data-driven insights to ensure that treatment costs are transparent, funds are allocated fairly, and the most urgent cases are prioritized.

advanced medical assistant application that utilizes Retrieval-Augmented Generation (RAG) with the Falcon Large Language Model (LLM) to provide accurate and context-aware medical information. Features Audio Interaction Endpoint Speech-to-Text (S2T) conversion LLM processing using Falcon Text-to-Speech (T2S) conversion for audible responses Text-based Interaction Endpoint Direct text input LLM processing using Falcon Text output Retrieval-Augmented Generation (RAG) Enhances responses with relevant medical knowledge Improves accuracy and context-awareness of the AI uses a Retrieval-Augmented Generation (RAG) architecture: User input (text or transcribed audio) is processed. Relevant medical information is retrieved from the Qdrant vector database. The Falcon LLM generates a response based on the user query and retrieved information. The response is returned as text or converted to speech.