NeuroGuard AI

Inspiration

Concussion recovery is rarely linear. Symptoms can change from day to day, while patients and caregivers may struggle to understand what to monitor, what activities are appropriate, and when professional help is needed.

We were inspired to build NeuroGuard AI to make daily recovery information easier to record, understand, and communicate. The goal was to create a calm, safety-first companion that supports patients, caregivers, and clinicians without replacing professional care.

What it does

NeuroGuard AI provides a guided daily concussion recovery check-in.

Users can:

• Describe symptoms in their own words.
• Answer optional questions about headache, dizziness, fatigue, sleep, mood, activity tolerance, vomiting, and warning signs.
• Receive a structured symptom summary.
• View a conservative risk assessment.
• See a five-stage recovery plan.
• Review evidence supporting the guidance.
• Track recovery across multiple check-ins.
• See whether recovery is improving, stable, or worsening.
• Generate a caregiver or clinician handoff summary.
• Start a new timeline for a new person or injury.

When urgent warning signs are reported, NeuroGuard prioritizes safety escalation instead of presenting normal recovery advice.

NeuroGuard AI is an educational decision-support prototype. It does not diagnose, treat, or replace professional medical care.

How we built it

We built NeuroGuard AI as a full-stack web application using:

• Python
• FastAPI
• Streamlit
• Pydantic
• SQLite
• FAISS
• Render

The application includes:

• Deterministic local symptom extraction.
• History-aware risk scoring.
• Conservative five-stage recovery planning.
• Local evidence retrieval from a concussion guideline corpus.
• Safety guardrails for emergency warning signs.
• Privacy-conscious logging.
• Offline regression, safety, and quality benchmarks.

The frontend combines natural-language input with optional guided questions. Each successful check-in advances the recovery timeline automatically and preserves previous history.

We designed the experience around a simple flow:

1. Describe what changed.
2. Clarify the symptoms.
3. Understand the safest next step.

Challenges we ran into

The biggest challenge was balancing usefulness with safety. Health-related software must not create false reassurance or overstate certainty.

We addressed this by:

• Prioritizing red-flag symptoms.
• Making emergency escalation visually prominent.
• Suppressing normal progression when urgent signs appear.
• Using conservative recovery recommendations.
• Including clear limitations and medical disclaimers.
• Avoiding claims of diagnosis or clinical validation.
• Validating structured results before displaying them.

Another challenge was representing recovery over time. A single score does not tell the complete story, so we added automatic day progression, recovery stages, symptom trends, activity response, historical check-ins, and handoff summaries.

We also designed the interface for users who may be tired, overwhelmed, or experiencing difficulty concentrating.

Accomplishments that we're proud of

We are proud that NeuroGuard AI turns a vague daily symptom report into a structured and understandable recovery view.

Key accomplishments include:

• A working end-to-end deployed application.
• Automatic recovery-day progression.
• Preserved multi-day recovery history.
• A clear reset flow for new people or injuries.
• Emergency escalation for synthetic red-flag scenarios.
• Evidence-linked recovery guidance.
• A caregiver and clinician handoff summary.
• Privacy-conscious default logging.
• A responsive and accessible check-in interface.
• Offline benchmark results showing 100% accuracy across the included evaluation cases.
• Successful deployment with the frontend and recovery service communicating correctly.

These benchmarks demonstrate consistency within our included test cases, not clinical validation.

What we learned

We learned that responsible health technology is not only about generating an answer. It is also about:

• Communicating uncertainty clearly.
• Making the safest next action understandable.
• Separating education from diagnosis.
• Designing for caregivers and healthcare professionals as well as patients.
• Protecting sensitive information by default.
• Testing emergency and unsafe scenarios explicitly.
• Treating usability as part of safety.
• Showing trends and context instead of relying on one isolated score.

We also learned that small interaction decisions—such as automatic timeline progression, clear warning states, and optional structured questions—can significantly improve the usefulness of a health application.

What's next for NeuroGuard AI

Future development will focus on making NeuroGuard more useful in real-world recovery workflows while maintaining strong safety boundaries.

Potential next steps include:

• Persisting recovery timelines securely across sessions.
• Adding age- and context-aware guidance.
• Supporting separate return-to-learn and return-to-activity pathways.
• Adding detailed activity tolerance tracking.
• Supporting clinician-reviewed recovery summaries.
• Conducting usability testing with patients, caregivers, and healthcare professionals.
• Expanding the evidence corpus with regularly reviewed clinical guidance.
• Adding multilingual and accessibility improvements.
• Exploring integrations with established clinical workflows under professional oversight.

NeuroGuard AI will remain a support tool—not a replacement for clinical evaluation, diagnosis, or emergency care.

Built With

• clinical-decision-support
• concussion-recovery
• data-validation
• digital-health
• evidence-based-care
• faiss
• fastapi
• healthcare-accessibility
• healthtech
• human-centered-design
• machine-learning
• mental-health
• patient-safety
• physical-health
• pydantic
• python
• recovery-planning
• render
• responsible-ai
• risk-assessment
• sqlite
• streamlit
• symptom-tracking
• web-application