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Open-source LLMs show promise for automated pathology report extraction · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a zero-shot, agentic workflow using open-source Large Language Models (LLMs) to extract crucial information from lung pathology reports. This method aims to automate the population of 13 College of American Pathologists synoptic fields, a task traditionally requiring manual effort and prone to errors. While a supervised baseline achieved a Micro-F1 score of 0.960, the best performing zero-shot LLM, GPT OSS 20B, reached a Micro-F1 of 0.893, demonstrating its capability to accurately extract complex relations without specific training. AI

IMPACT This research suggests open-source LLMs can provide a cost-effective solution for automating critical data extraction in medical pathology.

RANK_REASON Research paper detailing a novel application of LLMs for information extraction from clinical narratives.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Open-source LLMs show promise for automated pathology report extraction · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Aman Pathak, Cheng Peng, Mengxian Lyu, Ziyi Chen, Reema Solan, Sankalp Talankar, Yasir Khan, Hiren Mehta, Aokun Chen, Yi Guo, Yonghui Wu ·

    Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives

    arXiv:2606.19852v1 Announce Type: new Abstract: Information extraction from pathology reports is essential for cancer staging, tumor registry population. Yet key data remains embedded in narrative reports, making manual extraction labor-intensive and error-prone. Traditional supe…

  2. arXiv cs.CL TIER_1 English(EN) · Yonghui Wu ·

    Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives

    Information extraction from pathology reports is essential for cancer staging, tumor registry population. Yet key data remains embedded in narrative reports, making manual extraction labor-intensive and error-prone. Traditional supervised Natural Language Processing pipelines add…

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