3 minute read

Building an Interactive Diagnostic Tool with Claude’s LLM API and Jupyter Widgets

Introduction

In the field of healthcare, early and accurate diagnosis plays a crucial role in providing effective treatment to patients. With the advancements in artificial intelligence (AI) and natural language processing (NLP), it is now possible to create intelligent diagnostic tools that can assist medical professionals and empower patients. In this article, we will explore how to build a basic interactive diagnostic tool using Claude’s LLM API, the Python Anthropic package, and Jupyter Widgets.

Prerequisites

Claude account and API key

Go to the Anthropic Console to create your key. If you haven’t created a Claude account yet, do it when prompted to; it’s easy!

Python Packages

To get started, make sure you have Python and Jupyter Notebook installed on your machine. You will also need the Anthropic package, which provides a convenient way to interact with Claude’s LLM API. We’ll also need jupyter widgets. They can all be installed via:

pip install anthropic jupyterlab jupyterlab-widgets ipywidgets

Jupyter notebook

Start jupyter lab and create a new Jupyter Notebook:

  jupyter lab

Create a new cell (as below) to:

  • Import necessary code from modules
  • Create the anthropic Client with your API_KEY. In this example, it is stored in an environment variable so it is not published with this example.
  • Create the basic diagnose function, which accepts a string of symptoms, which can be separated by commas. This function uses the anthropic Client to communicate with the Claude API to obtain a basic diagnosis.
import os

import anthropic
from ipywidgets import widgets
from IPython.display import display

client = anthropic.Client(api_key=os.getenv('CLAUDE_API_KEY'))

PROMPT = """
Given the following symptoms: "{}", what are the possible diagnoses?
"""
def diagnose(symptoms):
    prompt = PROMPT.format(symptoms)
    message = client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=1024,
        messages=[
            {"role": "user", "content": prompt}
        ]
    )
    return message.content

User Interface

Creating the User Interface with Jupyter Widgets Now, let’s create an interactive user interface using Jupyter Widgets to accept symptoms from the user and display the possible diagnoses.

In the cell below, we create a simple UI with 3 components:

  • a text area widget for entering symptoms
  • a submit widget for triggering the diagnosis via the local on_submit_click function.
  • an output widget for displaying the results
DISCLAIMER_MSG = "Please note that this is not a definitive"
DISCLAIMER_MSG += " diagnosis. Always consult a healthcare"
DISCLAIMER_MSG += " a healthcare professional for proper evaluation"
DISCLAIMER_MSG += " and treatment."
SYMPTOM_LBL = "Describe your symptoms, separated by commas..."

def symptom_checker_widget():
    """
    An interactive interface, created with Jupyter widgets
    """

    caption = widgets.HTML("<h3>Diagnostics via Claude</h3>")
    
    symptom_input = widgets.Textarea(
        description='Symptoms',
        placeholder=SYMPTOM_LBL,
        layout={'width':'80%', 'height':'80px'}
    )
    
    diagnose_button = widgets.Button(description="Diagnose")
    output = widgets.Output(layout={
        'border': '1px solid black',
        'min_height':'80px',
    })
    output.append_stdout('Diagnosis will appear here.')

    def on_submit_click(b):
        with output:
            output.clear_output()
            print('Diagnosing...')
            symptoms = symptom_input.value
            diagnosis = diagnose(symptoms)
            possible_conditions = [m.text for m in diagnosis]            
            for condition in possible_conditions:
                print(f"- {condition}")
            print(f"\n\nDISCLAIMER_MSG")
            

    diagnose_button.on_click(on_submit_click)

    display(caption, symptom_input, diagnose_button, output)

Running the tool

To run the interactive diagnostic tool, execute the following cell, which simply calls the symptom_checker_widget function defined above. The user interface will appear, allowing users to enter their symptoms and click the “Diagnose” button to receive possible diagnoses based on the symptoms provided.

symptom_checker_widget()

Conclusion

In this article, we explored how to build an interactive diagnostic tool using Claude’s LLM API, the Python Anthropic package, and Jupyter Widgets. By leveraging the power of AI and NLP, we created a simple interface that accepts symptoms and provides possible diagnoses.

This diagnostic tool can serve as a valuable resource for both medical professionals and patients, aiding in the early identification of potential health issues. However, it is essential to note that this tool is not a substitute for professional medical advice and should be used in conjunction with proper medical evaluation and treatment.

It is also by no means a complete diagnostics tool. As you continue to develop and refine the diagnostic tool, consider incorporating additional features such as symptom validation, medical terminology recognition, and integration with medical knowledge bases to enhance its accuracy and usability. You might also consider tracking the diagnostic session, so it can be conversational and used to refine a diagnosis. Another feature that might be useful is suggesting other symptoms that might appear along with the submitted ones. This is useful for a patient who doesn’t remember all the symptoms, or might think they aren’t worth mentioning.

Remember to handle user input securely, ensure the privacy of sensitive health information, and provide appropriate disclaimers regarding the limitations and intended use of the diagnostic tool.

By combining the power of Claude’s LLM API, Python, and Jupyter Widgets, you can create innovative healthcare applications that have the potential to revolutionize patient care and empower individuals to take a more active role in managing their health.

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