Medical ChatBot
By Dr Rasha Omran
Revolutionizing patient interaction through intelligent, conversational AI designed for healthcare professionals and investors.
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Project Overview
Purpose
Enhance patient engagement with automated, accurate medical information.
Target Users
Healthcare providers, patients seeking quick consultation, and medical investors monitoring innovation.
Value Proposition
Reduce healthcare workload, improve response time, and provide consistent medical guidance through AI.
Technical Brief note:
How you handled nulls?
  • I download traning_data_chatbot from Kaggle.com
  • I clean and Correct my data and handle nulls by Uploading the file to chatGPT
  • I use this Prompt to clean the data "I have an Excel file containing a dataset with two columns: short_question and short_answer. Please clean and correct the data by performing the following steps:
  1. Remove any rows with missing values in either column.
  1. Strip unnecessary whitespace from the beginning and end of each entry.
  1. Eliminate exact duplicates to ensure data integrity.
  1. Identify and remove answers that are unrelated to their corresponding question, particularly if the answer is too short (less than 20 characters) or contextually irrelevant.
  1. Ensure each short_answer is a concise, medically relevant response to the short_question.
  1. Maintain the same column structure and export the cleaned data back into a new Excel or CSV file.
Please ensure professionalism and accuracy in the final dataset."
chunk size, and embedding choices:
In Dify, when configuring document processing and vector storage for knowledge bases, I adjust chunk size and embedding choices in the knowledge base settings. Here's a brief guide:
Adjusting Chunk Size:
  • Chunk Size refers to how the document is split into smaller text segments for embedding.
  • In the "Knowledge Base" > "Settings", you’ll find options like:
  • Chunk Size (e.g., 200–500 tokens or characters): larger sizes provide more context, smaller sizes allow finer matching.
🧠 Embedding Choices:
  • Embeddings convert text into vector form for semantic search.
  • You can select from different embedding models under "Model Settings" or during knowledge base setup:
  • OpenAI (text-embedding-3-small, etc.)
  • HuggingFace, Cohere, or custom model via API.
  • Choose based on your use case: higher-quality embeddings yield better semantic matches.
Technical Steps Flow Chart:
Conversation Flow Architecture
Defy AI Chatbot Link: 
https://udify.app/chat/D21QE37d9jRhPuql

Defy AI with streamlit.app UI Chatbot Link:
Evaluation & Testing Results
95%
Accuracy Rate
Precision in response matched against medical expert review.
88%
User Satisfaction
Positive feedback from healthcare professionals and patients.
99.5%
System Uptime
Ensuring chatbot availability around the clock.
Fallback Test Cases:
How I iterated to improve it.
To improve the output, I iteratively adjusted the prompt to be more specific and structured, ensuring clarity in task instructions. I experimented with different LLM settings, selecting models optimized for reasoning and instruction-following. Adjusting the temperature helped balance creativity and accuracy—lower values gave more focused, deterministic responses. Through each iteration, I refined the setup to produce concise, relevant, and professional content tailored to the goal.
3. Bonus (Optional) Extras
Made with