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New AI workflow extracts oncology data with 85% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed the Nimblemind Multi-Agent System (nMAS), a workflow designed to extract clinically relevant oncology information from fragmented patient documentation. This system aims to convert unstructured text into structured data, preserving clinical context and enabling accurate attribution across various medical details. In a retrospective evaluation using 230 de-identified oncology documents, nMAS achieved an F1 score of 85.0%, significantly outperforming a MiniMax M2.5 comparator which achieved 66.4%. The findings suggest that nMAS is a feasible solution for transforming complex medical records into usable structured data. AI

IMPACT This system could streamline clinical data abstraction, potentially improving cancer registry accuracy and research capabilities.

RANK_REASON The item is an academic paper detailing a new AI system and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI workflow extracts oncology data with 85% accuracy

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The item is an academic paper detailing a new AI system and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Daniel Kang, Michelle Hu, Soorya Ram Shimgekar, Shayan Vassef, Yufan Wang, Anit Kumar Sahu, Munmun De Choudhury, Vedant Das Swain, Christian Poellabauer, Li Yan Khor, Koustuv Saha, Robert Wojciechowski, Elliot Kidd, Piyum Zonooz, Navin Kumar ·

    From Analytics to Tumor Boards: An Evidence-Linked Multi-Agent Workflow for Oncology Feature Extraction

    arXiv:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and requiring accurate attribution across specimens, tumors, biomarkers, and time poin…

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