Lab Result Context Explainer

WHAT IT DOES

AI-powered lab result explainer that translates complex laboratory test results into plain-English explanations understandable to patients and non-experts. The script reads lab result data from the current page—test names, values, reference ranges, units, and flags like high/low/abnormal—and generates an easy-to-understand explanation of what the results mean in context. The AI explains each abnormal or notable result, describing what the test measures, what the result indicates, and whether it falls outside the normal range. You can customize the explanation length, reading level, and toggle inclusion of reference ranges, test descriptions, and potential implications. The user reviews the output before copying or using it.

WHERE IT RUNS

Works on all lab result pages (match pattern *://*/*), including patient portal lab sections, pathology reports, blood test results, hospital portals, and any page containing laboratory test data. Also works on email lab result notifications and printable lab reports.

HOW TO USE

Install the script and the Lab Result Context Explainer appears in the panel. Configure your preferred summary length, reading level, and toggle inclusion of reference ranges, test descriptions, and implications using the settings controls. Click "Explain" to analyze the current page. The AI extracts lab result data and generates a plain-English explanation. Review the output in the panel. Click "Copy to Clipboard" to copy the explanation for personal use, patient communication, or discussion with a healthcare provider. Adjust the settings and regenerate if you want a different reading level or detail level.

REAL MECHANISM: DOM CONTENT EXTRACTION WITH AI PLAIN-LANGUAGE EXPLANATION AND REVIEW GATE

This script uses a hybrid extraction approach that prioritizes structured content when available. First, it checks for WebMCP (navigator.modelContext), a W3C proposal that exposes structured tools on the page. If available, it calls getContent() to obtain structured lab data. If WebMCP is not present—the common case today—it falls back to DOM scraping using targeted selectors for lab result sections (lab-results, test-results, lab-report, pathology-results, blood-results, and test-results-table). It also scans table structures for content matching "test", "result", "value", "range", "unit", "normal", "high", or "low" patterns. The extracted text is sent to an AI backend via GM_xmlhttpRequest, which processes it with a system prompt that includes the user's reading level, summary length, and inclusion preferences. The AI returns a plain-English explanation (150-250 words depending on the selected length) that explains each abnormal or notable result, what the test measures, what the result indicates, and whether it falls outside the normal range, with a disclaimer that it is not a diagnosis. The response is displayed in a bordered container for review before copying. Copy-to-clipboard uses GM_setClipboard. All settings (summary length, reading level, includeRanges, includeDescriptions, includeImplications) are persisted via GM_setValue. The script enforces a review-before-use pattern: the user must read and approve the AI output before copying it.

THE PANEL

Drag the panel anywhere on screen by clicking and holding the header. It snaps to screen edges when released. Collapse it to a small bubble by clicking the minimize button, and expand it back by clicking the bubble. Resize the panel from any corner. Press Escape to close the configuration panel. Your position and size preferences are saved automatically per site.

PLEASE NOTE

This script requires the free BotGentz browser extension and an active AI subscription for the explanation service. AI-generated explanations should be reviewed for accuracy before use—the AI may misinterpret lab data or omit important details. This is for informational purposes only and not a diagnosis—always consult a healthcare professional for medical decisions. The script reads the current page content and sends it to the AI backend for processing; no data is stored or logged. Websites change frequently; if the script stops detecting lab content, check for updates to the extraction selectors.