The $0-per-report feature: a patient-friendly radiology explainer that runs on the clinic's own laptop

A developer built PlainRay, an open-source tool that converts radiology reports into plain-language patient explainers using a local open-weight model (gemma3:4b via Ollama) running on clinic hardware, with no cloud dependency and zero per-report cost. The tool enforces JSON output through a validator and grades readability with a stdlib Flesch-Kincaid implementation, lowering sample reports from roughly 10-12 grade levels to 6.7-7.6. The developer notes no real patient reports have been tested yet, only three synthetic samples.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle https://dev.to/challenges/hacktoberfest-week2-2026-10-12 . PlainRay https://github.com/4-thkind/plainray turns radiology reports into plain-language patient explainers — on the clinic's own machine, with an open-weight model, for zero rupees per report. It reads a report and produces, in one pass: Who would pay for this: the kind of clinic that runs two radiologists, one front desk, and a stack of reports whose first word is usually "IMPRESSION:". Behind that desk sits a person who answers "what does effusion mean?" eleven times a week. Every hosted "AI patient portal" wants a per-report meter and a copy of the report on someone else's server. PlainRay flips it: flat license, zero usage cost, nothing ever leaves the machine. The side-hustle math that makes it a real product instead of a demo: the clinic already owns the laptop, and the model runs on an RTX 4060. So the unit economics are: model = free forever, electricity = what the lamp costs, product = one flat fee per workstation. There is no other version of this offer where the cost driver of the whole business is $0 — that's only possible because the model is an open-weight one you can download. Three synthetic reports are in the repo samples/sample reports.json — no real PHI is ever committed . Here is the actual flow, captured locally while the machine was offline for everything else: The second frame is the mammography screening sample — the star of the show, because screening reports are the least readable of all see it here https://raw.githubusercontent.com/4-thkind/plainray/main/demo/02-mammo-explained.png . No login, no cloud, no network. Unplug the ethernet cable and it keeps working — that is the demo. Run it yourself: git clone https://github.com/4-thkind/plainray ollama pull gemma3:4b one time, ~3 GB python -m plainray serve http://127.0.0.1:7860 python -m plainray explain --file report.txt JSON on stdout python -m plainray selftest prompts + validator + readability, no model needed Everything lives in the 4-thkind/plainray https://github.com/4-thkind/plainray repo: plainray/llm.py — a minimal local Ollama client in stdlib urllib , ~40 lines. No pip, no SDK. plainray/prompts.py — the patient-safety prompt, readable in plain text by whoever deploys it. plainray/app.py — CLI explain / serve / selftest and a validator that enforces the JSON shape: caps list lengths, dedupes glossary terms, refuses an empty summary. plainray/readability.py — a Flesch-Kincaid grader in stdlib. Numbers, not promises. static/index.html — the UI; the server binds to 127.0.0.1 on purpose. validate rebuilds the JSON from scratch. A 4B model | Sample report | Raw grade | PlainRay explainer | |---|---|---| | Chest X-ray, consolidation | 10.4 | 7.6 | | Knee MRI, meniscus tear | 10.0 | 7.6 | | Mammography screening | 12.3 | 6.7 | http.server . Clinic IT never touches a package manager. PLAINRAY MODEL : gemma3:12b on a bigger box, or the medical-tuned sibling medgemma — same code, same prompt. This project was built in one working session with an AI coding agent — I set the problem, the constraints "small, specific, useful, and it must actually run on this laptop" and reviewed everything it produced. The short form of that session — constraints discovered first, the decisions worth keeping, and what was hard — is checked into the repo as AGENT NOTES.md https://github.com/4-thkind/plainray/blob/main/AGENT NOTES.md . No DevRelay install for this build; the notes file is the session, honestly summarized. Honest version: my first real user test hasn't happened yet. During this challenge window I built the product and ran it end-to-end on my own machine against three synthetic reports; nobody's real report went through it, because the right first user is a clinic that will trade one afternoon for a demo, and setting that up properly takes longer than the window. Rather than report a fake ten, here is the actual next step: one call to my cousin's ortho clinic, a printed example made from samples/ , and two questions I'll log verbatim in the repo — would you pay, and how much per year? If you're a radiologist or run a small clinic and want to be user 1: the demo runs from a fresh clone in under two minutes, and my contact is on my profile. Best Use of Gemma — Gemma 3 4B runs entirely on-device through Ollama, powers the whole product, and the architecture keeps gemma3:12b / medgemma as one-variable drop-ins. Solo build — no teammates.