Building MedX came with multiple challenges, from ensuring medical accuracy to optimizing AI performance. Here are some key obstacles we faced and how we overcame them:

⚙️ 1. Ensuring Medical Accuracy
Challenge: AI-generated responses needed to be reliable and medically accurate to prevent misinformation.
Solution: We used Retrieval-Augmented Generation (RAG) with Pinecone to fetch data only from verified medical sources, ensuring trustworthiness.
🔍 2. Optimizing Search for Fast Medical Retrieval
Challenge: Traditional keyword-based searches weren’t effective in retrieving precise medical insights.
Solution: Implemented vector-based search (Pinecone) to store and retrieve data efficiently, improving response speed and accuracy.
📊 3. Managing Large Medical Datasets
Challenge: Medical texts are often complex and unstructured, making it difficult for AI models to process them effectively.
Solution: We pre-processed medical documents, converted text into vector embeddings, and stored them in Pinecone for faster and structured retrieval.
🔐 4. Handling User Data Securely
Challenge: Storing user health queries while maintaining privacy and security was critical.
Solution: We used MongoDB for encrypted storage and ensured secure API communication to protect user data.