MediTrack

The problem. People collect lab results as a pile of cryptic PDFs. The apps that make them readable want you to upload your bloodwork to their servers — so the fix for confusion costs you your medical privacy. MediTrack refuses that trade: it turns lab PDFs into plain-language, trended insights while every piece of health data stays on your own device. No accounts, no servers, no telemetry.

The hard part. "Local-only" and "AI analysis" pull in opposite directions — a plain-language summary normally means shipping the results to a model in the cloud. I kept both by drawing the privacy boundary inside the device: results are parsed and stored in an encrypted on-device SQLite database, and the one time the network is touched — the optional AI summary — the payload is stripped of identifying data on-device first. The model sees values, never a patient.

The approach. One canonical JSON schema code-generates the models for both the Vue 3 PWA and the Flutter app, so web and mobile can never drift apart. On-device OCR (Tesseract WASM) maps an uploaded PDF or photo onto a 70-test catalogue for review before save; reference-range markers and 12-month trend charts do the interpretation. A Clean Architecture split keeps the presentation layer from ever reaching into storage or the AI client directly, and mobile encrypts at rest with SQLCipher (AES-256).

The outcome. A genuinely private health tracker that still feels modern — family profiles, retest reminders, password-gated encrypted backups, idle and tab-blur auto-lock, and full Arabic/English RTL enforced by a CI gate that fails the build on a physical-direction CSS rule.