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User question:
How to build an agentic cardiologist chatbot in Bubble?
ChatGPT o1 answer:
Thought about building a cardiologist chatbot for 5 seconds
Below is a detailed roadmap for building an “agentic cardiologist” chatbot using Bubble. This plan assumes familiarity with Bubble’s interface, basic workflows, and some integration with external AI services such as OpenAI’s API. The approach emphasizes building a domain-specific (cardiology-focused) conversational agent that can provide educational guidance, triage symptom input, and potentially interact with external data sources in a controlled, compliant manner. Note that any medical-related chatbot should include clear disclaimers and must not be used as a substitute for professional medical advice.
Key Considerations Before You Start
Compliance & Liability:
Disclaimers: Prominently display a disclaimer that this chatbot is not a licensed physician, and users must always seek professional medical attention for serious or urgent concerns.
HIPAA & Data Privacy: If dealing with personal health information (PHI), you must be aware of and compliant with all relevant healthcare data privacy regulations (e.g., HIPAA in the U.S.). Bubble itself is not HIPAA-certified out-of-the-box, so consider what data you store and ensure that no identifiable PHI is stored insecurely.
Content & Knowledge Base:
Determine whether the chatbot will use a large language model (LLM), a custom fine-tuned medical model, or a curated Q&A knowledge base.
For cardiology-specific knowledge, you may consider using a retrieval-augmented generation (RAG) approach—where the LLM references vetted cardiology literature or guidelines—ensuring medically accurate and safe responses.
Agentic Capabilities:
“Agentic” implies going beyond static Q&A. Your chatbot might:
Suggest follow-up questions based on symptoms.
Guide users through risk assessment questionnaires.
Integrate with external databases of cardiology guidelines (e.g., via APIs) to provide updated recommendations.
Consider using a workflow where the chatbot can “call” certain data endpoints or run decision-trees behind the scenes before responding.
Step-by-Step Implementation
Step 1: Set Up Your Bubble Environment
Create a New App: Start by creating a new Bubble application from your Bubble dashboard.
Design the UI:
Add a page (e.g., index) that features:
A chat interface: A repeating group to display messages (User and Bot).
An input box for the user’s query.
A send button or an action triggered by the Enter key.
Include a visible disclaimer on the page. For example, a text block stating: “This chatbot is for educational purposes only and is not a replacement for professional medical advice.”
Step 2: Integrate an LLM or Medical AI API
Choose Your AI Provider:
Commonly: OpenAI’s API (e.g., GPT-4) or a specialized medical LLM such as Med-PaLM (if accessible).
For cardiology-specific data, consider fine-tuning or providing the model with system prompts that direct it towards authoritative cardiology guidelines (e.g., ACC/AHA guidelines).
Set Up API Calls in Bubble:
Use Bubble’s API Connector plugin to connect to the AI provider’s endpoint.
Configure the POST request with necessary headers (API key) and payload (prompt, temperature, max tokens, etc.).
For improved reliability and safety, set a system message instructing the model:
Example: “You are a cardiologist chatbot. You provide general educational information about heart health, common conditions like hypertension, arrhythmias, coronary artery disease, and lifestyle recommendations. You do not provide personal medical advice. Always encourage consulting a professional for symptoms or treatments.”
Consider adding a knowledge injection step: You could have a curated set of cardiology FAQs or guidelines stored in your Bubble database and prepend relevant excerpts to the prompt, allowing the LLM to reference them.
Step 3: Building Workflows for Conversation
Sending User Input to the LLM:
When the user submits a message (clicks ‘Send’ or presses Enter), trigger a workflow:
Create a new “Message” thing in your database with the user’s text.
Send an API call to your LLM provider, including both the conversation history and relevant reference text.
On success, store the returned answer in your database and display it in the chat interface.
Maintaining Context:
To maintain a coherent conversation, you can store previous messages and send them as context in each subsequent API call.
Limit context to the last few user-bot turns to stay within token limits and ensure cost-effectiveness.
Agentic Behavior Through Decision Trees & External Data Calls:
Suppose the user describes specific symptoms (e.g., chest pain). Your workflow can:
Check the symptom against a small logic tree in Bubble (e.g., if chest pain + shortness of breath = Suggest emergency care disclaimer).
If the user mentions medications or conditions, have Bubble fetch relevant standard guidelines stored in your database and prepend them to the AI prompt.
Integrate additional APIs for external data, such as a drug database or a heart disease risk calculator API. After getting a user’s input on various lifestyle and medical history factors, send that data to an external risk calculator and incorporate the results into your LLM prompt.
Step 4: Fine-Tuning the Conversation & Safety Mechanisms
Content Filtering & Safety Checks:
Use the LLM’s moderation endpoint (if available) or Bubble workflows to screen user input for emergencies. If detected (e.g., “I’m having severe chest pain right now”), respond with a safe-harbor emergency message (“Please call your local emergency number immediately.”) and stop further general Q&A.
Include logic in your workflow: If the user’s prompt contains red-flag symptoms, override the LLM response and present a fixed message advising immediate medical attention.
Iterative Prompt Engineering:
Improve the quality of answers by refining the system and user prompts.
Add directives like “Respond concisely and in layman’s terms” or “Offer general guidance from authoritative cardiology guidelines (e.g. ACC/AHA) without personalizing too closely.”
Test edge cases to ensure the chatbot remains consistent and safe.
Step 5: UI Polish & User Experience
Responsive Design: Ensure the chat UI is mobile-friendly, as many users will access it on smartphones.
Message Styling:
Differentiate user messages and bot messages visually.
Include small icons or avatars (e.g., a stethoscope icon for the bot).
Loading Indicators: Show a loading spinner or typing indicator while awaiting the LLM’s response.
Persistent Sessions (Optional):
If the user logs in, store their conversation history and allow them to return to past sessions.
Always remind them that this is for educational use only.
Step 6: Testing & Iteration
User Testing:
Have testers ask a variety of cardiology-related queries.
Check for accuracy, clarity, and helpfulness.
Validate that no sensitive PHI is stored improperly.
Data Validation:
Periodically review stored messages (if anonymized and allowed) to ensure the bot is giving safe and medically consistent answers.
Iterate on Your Prompt & Logic:
If the chatbot suggests something too general or unsafe, strengthen the guardrails in the system prompt.
Update the underlying database of FAQs and guidelines.
Step 7: Deployment & Maintenance
Deploy Your Bubble App: Once satisfied, deploy to Live.
Monitor Costs: Keep track of API usage and optimize prompts for efficiency.
Continuous Improvement:
Update guidelines and references as new cardiology recommendations emerge.
Consider versioning the chatbot’s logic and prompt engineering as your domain knowledge base grows.
Summary
Building an agentic cardiologist chatbot in Bubble involves careful planning around compliance, data handling, and domain-specific content. By leveraging Bubble’s no-code front-end and workflow logic, combined with a robust LLM integration (e.g., via the API Connector), you can create a user-friendly chat interface. Add decision trees, external data integrations, and prompt engineering to refine its “agentic” capabilities. Always emphasize safety, disclaimers, and the educational nature of the bot’s advice.
