AppointReady: Simulated Pre-visit Intake Demo built using MedGemma
Healthcare providers often seek efficient ways to gather comprehensive patient information before appointments. This demo illustrates how MedGemma could be used in an application to streamline pre-visit information collection and utilization.
The demonstration first asks questions to gather pre-visit information.
After it has identified and collected relevant information, the demo application generates a pre-visit report using both collected and health record information (stored as FHIR resources for this demonstration). This type of intelligent pre-visit report can help providers be more efficient and effective while also providing an improved experience for patients compared to traditional intake forms.
At the conclusion of the demo, you can view an evaluation of the pre-visit report which provides additional insights into the quality of the demonstrated capabilities. For this evaluation, MedGemma is provided the patient's reference diagnosis, allowing MedGemma to create a self-evaluation report highlighting strengths as well as areas for improvement.
Technical Architecture
This application is composed of several key components:
* Frontend: A web interface built with React that provides the user interface for the chat and report visualization.
* Backend: A Python server built with Gunicorn/Flask that handles the application logic. It communicates with the LLMs, manages the conversation flow, and generates the final pre-visit report.
* API called:
* MedGemma: Acts as the clinical assistant, asking relevant questions and summarizing information.
* Gemini:
AppointReady: Simulated Pre-visit Intake Demo built using MedGemma
Healthcare providers often seek efficient ways to gather comprehensive patient information before appointments. This demo illustrates how MedGemma could be used in an application to streamline pre-visit information collection and utilization.
The demonstration first asks questions to gather pre-visit information.
After it has identified and collected relevant information, the demo application generates a pre-visit report using both collected and health record information (stored as FHIR resources for this demonstration). This type of intelligent pre-visit report can help providers be more efficient and effective while also providing an improved experience for patients compared to traditional intake forms.
At the conclusion of the demo, you can view an evaluation of the pre-visit report which provides additional insights into the quality of the demonstrated capabilities. For this evaluation, MedGemma is provided the patient's reference diagnosis, allowing MedGemma to create a self-evaluation report highlighting strengths as well as areas for improvement.
Technical Architecture
This application is composed of several key components:
* Frontend: A web interface built with React that provides the user interface for the chat and report visualization.
* Backend: A Python server built with Gunicorn/Flask that handles the application logic. It communicates with the LLMs, manages the conversation flow, and generates the final pre-visit report.
* API called:
* MedGemma: Acts as the clinical assistant, asking relevant questions and summarizing information.
* Gemini: Role-plays as the patient, providing responses based on a predefined scenario.
* Gemini TTS: Generative text-to-speech model.
* Deployment: The entire application is containerized using Docker for easy deployment and scalability.
A high-level overview of the architecture:
Running the Demo Locally
To run this demo on your own machine, you'll need to have Docker installed.
Prerequisites
* Docker
* Git
* A Google Cloud project with the Vertex AI API enabled.
Setup & Configuration
Clone the repository:
bash
git clone https://huggingface.co/spaces/google/appoint-ready
cd appoint-ready
Configure environment variables:
This project uses an env.list file for configuration, which is passed to Docker. Create this file in the root directory.
ini
# env.list
GEMINI_API_KEY="your-gemini-token"
GENERATE_SPEECH=false or true
GCP_MEDGEMMA_ENDPOINT=medgemma vertex ai endpoint
GCP_MEDGEMMA_SERVICE_ACCOUNT_KEY="service-account-key json"
GEMINI_API_KEY: Key can be generated via aistudio.
GENERATE_SPEECH: Should the demo generate speech not found in cache. Default is false.
GCP_MEDGEMMA_ENDPOINT: Deploy MedGemma via Model Garden.
Execution
Build and run the Docker containers:
bash
run_local.sh
Access the application:
Once the containers are running, you can access the demo in your web browser at http://localhost:[PORT]. (e.g., http://localhost:7860).
Models used
This demo uses four models:
* MedGemma 27b-text-it: https://huggingface.co/google/medgemma-27b-text-it \
For this demo MedGemma-27b was deployed via Model Garden (https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma).
* Gemini: https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash \
We use Gemini to role play the patient while MedGemma plays the clinical assistant.
* Gemini TTS: https://ai.google.dev/gemini-api/docs/models#gemini-2.5-flash-preview-tts \
As an alternative consider using Clould Text-to-Speech: https://cloud.google.com/text-to-speech
* Veo 3: Was used to generate patient avatar animation https://gemini.google/overview/video-generation
Caching
This demo is functional, and results are persistently cached to reduce environmental impact.
Disclaimer
This demonstration is for illustrative purposes only and does not represent a finished or approved
product. It is not representative of compliance to any regulations or standards for
quality, safety or efficacy. Any real-world application would require additional development,
training, and adaptation. The experience highlighted in this demo shows MedGemma's baseline
capability for the displayed task and is intended to help developers and users explore possible
applications and inspire further development.