Radiograph Ready

Inspiration

A confident model answer can still be based on an unusable radiograph. Radiograph Ready makes image quality a separate, teachable gate before diagnosis.

What we built

The site presents a small de-identified pilot benchmark, model-comparison readouts, and an interactive readiness triage. Learners can inspect visible factors such as positioning, coverage, contrast, and artifacts, then see an explanation and a human-escalation path.

Technical implementation

The project is a responsive TypeScript research site with a deterministic triage rubric, seeded pilot data, accessible controls, and a front-end-only contribution handoff. No real patient file is uploaded by the demo. It is a research prototype, not a medical device, diagnostic tool, or clinical decision system.

Validation and ethics

The pilot uses de-identified material and keeps the claims narrow: compare quality-assessment behavior, not pathology accuracy. The next step is a larger preregistered benchmark with expert review. Codex/ChatGPT assisted with scaffolding, documentation, and testing; the README discloses AI assistance and the work remains explainable.

Why it matters

Radiograph Ready teaches a durable safety habit: verify the input before trusting the output.

Built With

• accessibility
• ai
• data-ethics
• github
• machine-learning
• react
• typescript