Steps

Step 1: Choosing the AI Model

We need a smart AI model (like ChatGPT or other LLMs) to understand doctors' questions and provide relevant answers. But the AI alone is not enough—it doesn’t automatically have access to patient data. So, we need a way to feed it the right information securely.

Step 2: Handling Patient Data Securely

We cannot just dump the entire database into the AI model whenever a doctor asks a question. Instead, we should:

  1. Understand the doctor’s question (What are they asking? Do they need a diagnosis suggestion, past medical history, lab results, etc.?)

  2. Retrieve only relevant patient data related to the question.

  3. Send only that relevant data to the AI for response generation.

This brings us to semantic search.

Step 3: Using Semantic Search

Semantic search is an advanced search technique that helps find meaningfully relevant data, even if the exact keywords aren’t used. Unlike basic keyword-based search, semantic search understands the context of a query.

How does it work?

  1. Convert all patient records into "embeddings" (vector representations of text).

  2. When a doctor asks a question, convert the question into an embedding too.

  3. Find the most relevant patient data by comparing the question embedding to stored embeddings.

  4. Retrieve and send only the most relevant patient information to the AI model.

For example:

  • Doctor asks: "Show me John's recent blood test results."

  • Semantic search retrieves only John's latest test results instead of searching for the exact phrase "blood test."

This optimizes the AI's response, reduces data sent, and keeps things efficient & secure.

Step 4: Sending Data to the AI

Now that we have only the needed information, we can send it to the AI along with the doctor's query, like this:

💬 Doctor's Question: "Does Sarah's condition indicate diabetes?" 🔍 Semantic Search Retrieves: Sarah's past glucose levels, weight history, symptoms, and family history. 🤖 AI Response (with context): "Based on Sarah’s glucose levels and symptoms, there is a possibility of diabetes. You may consider running an HbA1c test to confirm."

This makes the chatbot smarter, secure, and efficient.

implementation Steps

  1. Use a vector database to store embeddings for semantic search.

  2. Use an AI model to answer questions based on retrieved data.

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