{"paper_id":"ca6f1552-5067-4236-ad30-e91a02e9499a","body_text":"Computer Science > Human-Computer Interaction\n[Submitted on 26 Jan 2026 (v1), last revised 14 Feb 2026 (this version, v3)]\nTitle:EndoExtract: Co-Designing Structured Text Extraction from Endometriosis Ultrasound Reports\nView PDF HTML (experimental)Abstract:Endometriosis ultrasound reports are often unstructured free-text documents that require manual abstraction for downstream tasks such as analytics, machine learning model training, and clinical auditing. We present \\textbf{EndoExtract}, an on-premise LLM-powered system that extracts structured data from these reports and surfaces interpretive fields for human review. Through contextual inquiry with research assistants, we identified key workflow pain points: asymmetric trust between numerical and interpretive fields, repetitive manual highlighting, fatigue from sustained comparison, and terminology inconsistency across radiologists. These findings informed an interface that surfaces only interpretive fields for mandatory review, automatically highlights source evidence within PDFs, and separates batch extraction from human-paced verification. A formative workshop revealed that \\textbf{EndoExtract} supports a shift from field-by-field data entry to supervisory validation, though participants noted risks of over-skimming and challenges in managing missing data.\nSubmission history\nFrom: Haiyi Li [view email][v1] Mon, 26 Jan 2026 05:17:32 UTC (7,483 KB)\n[v2] Thu, 29 Jan 2026 06:14:41 UTC (7,483 KB)\n[v3] Sat, 14 Feb 2026 10:38:52 UTC (7,483 KB)\nReferences & Citations\nLoading...\nBibliographic and Citation Tools\nBibliographic Explorer (What is the Explorer?)\nConnected Papers (What is Connected Papers?)\nLitmaps (What is Litmaps?)\nscite Smart Citations (What are Smart Citations?)\nCode, Data and Media Associated with this Article\nalphaXiv (What is alphaXiv?)\nCatalyzeX Code Finder for Papers (What is CatalyzeX?)\nDagsHub (What is DagsHub?)\nGotit.pub (What is GotitPub?)\nHugging Face (What is Huggingface?)\nScienceCast (What is ScienceCast?)\nDemos\nRecommenders and Search Tools\nInfluence Flower (What are Influence Flowers?)\nCORE Recommender (What is CORE?)\narXivLabs: experimental projects with community collaborators\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\nHave an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.","source_license":"CC0","license_restricted":false}