CLARA: AI's Life-Saving Intervention

Clinical Logic Assessment & Reasoning Assistant

CLARA is a multimodal AI safety net dedicated to supporting clinical reasoning and helping identify diagnostic shadowing to optimize patient care.

The Problem: Cognitive Load & System Strain

• Diagnostic error impacts 12 million Americans annually [1].
• In women's health, the crisis is more severe: women are diagnosed an average 4 years later than men [2].
• A major factor is Diagnostic Shadowing [3]: the tendency to inadvertently attribute physical symptoms to psychiatric history.
• This is a recognized systemic issue-it's the result of Dual Process Theory being strained under immense clinical pressure.
  • Physicians operating under high cognitive load are forced to rely on System 1 (fast/heuristic thinking) just to keep up.
  • The analytical System 2 can be inadvertently bypassed, allowing necessary cognitive shortcuts to occasionally result in unintended clinical blind spots [4].

The Solution: A Cognitive Support Strategy

CLARA is built as an "External System 2"-a background AI ally powered by Gemini 3 Pro.

• Not just a scribe. Rather than merely transcribing conversations, CLARA supports the reasoning process behind the encounter.
• Functions as a safety net, continuously running in the background to reduce physician cognitive burden.
• Highlights potential diagnostic blind spots and offers gentle clinical nudges for review prior to patient discharge.
• Helps physicians maintain their highest standard of clinical accuracy, even when their mental bandwidth is exhausted.

Methodology & Impact

• Developed with Google AI Studio.
• Processes consented consultation audio and outputs standardized "Clinical Insights" (JSON).
• Converts subjective reports of being dismissed into objective, actionable data for the care team.
• Empowers equitable patient care while reducing cognitive fatigue and systemic risk for providers.

Getting Started

Prerequisites

• Node.js (latest LTS recommended)

Local Setup

1. Clone the repository and navigate to the project root.
2. Install dependencies:

   npm install

3. Create a .env file from the example and set your Google Cloud project details:

   cp .env.example .env

   GOOGLE_CLOUD_PROJECT=your-google-cloud-project-id
   GOOGLE_CLOUD_LOCATION=us-central1

4. Authenticate locally with Application Default Credentials (ADC):

   gcloud auth application-default login

5. Run the development server (Vite on :3000 + Express on :3001):

   npm run dev

6. Open http://localhost:3000 in your browser.

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Deploying to Google Cloud Run

The app is packaged as a single Docker container: the Express server serves the built Vite frontend and proxies all Gemini calls server-side, utilizing Application Default Credentials (ADC) to authenticate with Vertex AI.

Prerequisites

• Docker installed locally
• Google Cloud CLI installed and authenticated
• A Google Cloud project with billing enabled

One-command deploy (source deploy — no local Docker needed)

gcloud run deploy clara \
  --source . \
  --region us-central1 \
  --platform managed \
  --allow-unauthenticated \
  --set-env-vars GOOGLE_CLOUD_PROJECT=your-google-cloud-project-id \
  --set-env-vars GOOGLE_CLOUD_LOCATION=us-central1

Cloud Run builds the container using the Dockerfile, then deploys it. The deployed service relies on the default compute service account for Application Default Credentials. Ensure that the service account has the Vertex AI User role.

Manual build & push (optional)

# Build
docker build -t gcr.io/YOUR_PROJECT_ID/clara .

# Push
docker push gcr.io/YOUR_PROJECT_ID/clara

# Deploy
gcloud run deploy clara \
  --image gcr.io/YOUR_PROJECT_ID/clara \
  --region us-central1 \
  --platform managed \
  --allow-unauthenticated \
  --set-env-vars GOOGLE_CLOUD_PROJECT=your-google-cloud-project-id \
  --set-env-vars GOOGLE_CLOUD_LOCATION=us-central1

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References

1. Singh H et al. BMJ Qual Saf 2014.
2. Westergaard D et al. Nat Commun 2019.
3. Reiss S et al. Am J Ment Defic 1982.
4. Trimble M UMJ 2015.

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For more information or contributions, see the codebase and issues on GitHub.