Risk stratification
Estimates near-term risk to support screening decisions and care pathways.
Predictive Risk Intelligence | Chest X-ray First
Turn routine chest X-rays into 1 to 3 year lung cancer risk insights.
PneuScan AI is a clinical decision support tool that estimates near-term lung cancer risk from everyday chest X-rays. It helps healthcare teams prioritize follow-up screening with minimal workflow disruption.
Made for scale
Designed to run on high-volume imaging workflows
Trust focused
Risk band, explainability overlay, and report summary
Interactive preview
Risk Stratification Output
Estimated risk band
Moderate
42%
Moderate risk suggests closer follow-up based on clinical criteria.
- Risk band plus probability range
- Explainability overlay for clinician review
- Suggested follow-up pathway
Disclaimer: This is a demo UI. PneuScan AI supports clinical decision-making and does not diagnose disease.
What we do
PneuScan AI adds predictive risk intelligence to routine chest X-rays so health systems can identify elevated-risk patients earlier and prioritize screening resources more effectively.
Opportunistic screening
Uses X-rays already taken daily for checkups, clearances, and urgent care workflows.
Workflow ready output
Generates a clean report summary designed for clinical review and pilot deployment.
Key features
Built for real adoption with clear outputs, quality handling, and workflow-ready reporting.
Core Output
Short-term risk band
Converts routine chest X-rays into a 1 to 3 year risk band with a probability range to support decision-making.
- Low, Moderate, Elevated categories
- Probability range for context
- Next-step suggestion for screening
Trust Layer
Explainability overlay
Provides an interpretable overlay highlighting regions that influenced the model score for clinical review.
- Visual contribution map
- Report rationale summary
- Audit-friendly traceability
Deployment
Workflow-ready report
Outputs a clean one-page report designed to fit clinical workflows and enable validation pilots.
- Clear risk statement
- Follow-up guidance language
- Professional format
Quality-aware preprocessing
Standardizes noisy real-world X-rays to reduce variability across different machines and settings.
- Normalization and contrast standardization
- Lung region focus to reduce distractions
- Quality flags for low-quality inputs
Clinical safety and governance
Designed as decision support with guardrails and traceability to support responsible deployment.
- Risk stratification only, not diagnosis
- Model version and timestamp support
- Validation and audit readiness
High-volume readiness
Built for settings where imaging volume is high and staff time is limited.
- Fast inference design approach
- Report output to reduce extra steps
- Deployment-friendly architecture
Roadmap: CT refinement module
When CT is available, PneuScan can refine risk and support follow-up monitoring for flagged patients.
- CT refinement for higher resolution review
- Follow-up comparison over time
- Stronger clinical decision support
0
Primary modality for Phase 1
0
Risk horizon target in years
0
Extra scans required for scoring
How it works
Click a step to focus it. This is designed for simple clinical deployment workflows.
Important: PneuScan AI supports decision-making and does not replace clinical judgment.
Built for trust
Medical AI must be responsible, transparent, and designed for clinical adoption.
Responsible outputs
Risk-focused reporting designed to support appropriate follow-up decisions.
Explainability first
Clinicians can review what contributed to the estimate and interpret results more confidently.
Validation pathway
Designed for retrospective validation, external testing, and pilot studies in real settings.
About Us
We are third-year BS Computer Science students building PneuScan AI, a predictive risk intelligence tool that analyzes chest X-rays and estimates the likelihood of lung cancer risk within 1 to 3 years. Our goal is to support earlier screening decisions by turning routine imaging into actionable, clinician-friendly insights.
Our mission
Help healthcare teams identify elevated-risk patients earlier, prioritize follow-up screening, and reduce late-stage lung cancer outcomes through accessible and workflow-ready AI.
Chest X-ray first
1 to 3 year risk horizon
Decision support
Focused on building a practical medical AI product that fits real clinical workflows, prioritizes trust, and delivers measurable impact.
- Vision and roadmap
- Model direction and validation planning
- Product and clinical workflow design
Builds the platform foundation for secure uploads, model inference flow, and a clean UI that communicates results clearly.
- Web experience and UI engineering
- Model integration and deployment
- Data pipeline and system reliability
Request a clinical demo
Get a pilot-ready walkthrough of the risk report experience, integration approach, and validation roadmap.