AI assistants and search decision
Rash Scan
A technical user can implement a useful single-scan image-analysis workflow using open models and standard web tooling, but reproducing RashScan's claimed dermatologist-verified dataset, privacy polish and full Pro feature set (PDF quality, AI consultation, iOS app and branded trust) is not realistic without the vendor's proprietary data and investment.
Visit website↗$29.99/mo
$360/yr
Read off the official pricing page.
$100one-off60 h to build
$200/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 7 seats.
No open-source build does this yet
Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.
What a replacement has to do
- User uploads a photo → client resizes/validates image → server/model runs image analysis → map model output to ranked conditions, severity and care guidance → generate an unlockable PDF report or return limited free result in-browser.
What it still won’t have
- The vendor's dermatologist-verified training set and any curated proprietary labeling
- Established privacy UX (no account, device-tied scans) and any mobile app integrations
- Built-in PDF/report polish and AI consultation/chat features
- Brand trust and existing usage signals (10,000+ scans) claimed on the site
What remains hard
- Proprietary data
The AI is trained against thousands of dermatologist-verified images spanning 50+ common skin, hair and nail conditions.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 7 seats.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal RashScan replacement: implement a single-page React frontend and a Python FastAPI backend. Stack: React + Vite, FastAPI, Uvicorn, PostgreSQL (optional) for unlocked-scan receipts, and host a lightweight image-understanding model (convert OpenBMB/MiniCPM-V to ONNX/TorchScript) behind a REST endpoint. In-scope features: image upload (JPG/PNG/HEIC up to 10MB) with client-side resize/orientation, symptom form, server inference call, mapping rules that convert model output to a ranked list of 50+ conditions with severity and red-flag heuristics, free limited in-browser result and paid unlock flow (single-scan purchase via Stripe), and PDF export of full report. Out of scope: training a new dermatology dataset, building a mobile app, or regulatory/medical certification. Require: input validation, error handling, unit tests for mapping logic, integration test for upload→inference→PDF path, and automated deployment scripts (Dockerfile, basic cloud hosting instructions).
How we checked
How the score was reached
- Partly verdict base52
- 3 cited sources+3
- Price verified on pricing page+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score59
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productRashScan home
- official pricingRashScan pricing
- official docsRashScan app features
Integrity checks
What held up, and what did not.
