Quick overview Automatically retrieve recent PubMed articles, identify the three papers most worth reading first using AI, and generate structured clinical literature triage reports in Google Sheets and Google Docs. How it works Reads the first pending literature request from the Google Sheets "Search Requests" tab. Marks the request as “processing” in Google Sheets and builds the PubMed query parameters (keyword, max results, specialty, clinical question, and output language). Searches PubMed via the NCBI E-utilities API (esearch) and fetches the corresponding article abstracts and metadata as XML (efetch). Parses and normalizes the XML into a structured list of candidate articles including title, abstract, journal, publication year, DOI, and PubMed URL. Sends the aggregated candidate articles to Google Gemini to select and rank the best three papers and generate a structured clinical evaluation for each. Appends the three ranked papers with summaries and triage fields to a Google Sheets “Triage Results” tab. Generates a formatted triage report in Google Docs and updates the original request status to “completed” in Google Sheets.
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Google DocsGoogle Gemini Chat Model
Google SheetsWHTTP RequestRelated workflows
See all AI Automation→Draft personalized cold emails from Google Sheets using Gemini and HubSpot
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Generate weekly student progress reports with Gemini, Supabase, and WhatsApp
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Handle WhatsApp customer service chats with Google Gemini and Google Sheets
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