Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives
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Computer Science > Computation and Language
arXiv:2606.19852 (cs)
[Submitted on 18 Jun 2026]
Title:Prompt, Plan, Extract: Zero-Shot Agentic LLMs Workflows for Lung Pathology Extraction from Clinical Narratives
Authors:Aman Pathak (1), Cheng Peng (1), Mengxian Lyu (1), Ziyi Chen (1), Reema Solan (1), Sankalp Talankar (1), Yasir Khan (1), Hiren Mehta (2), Aokun Chen (3), Yi Guo (1), Yonghui Wu (1)
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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 supervised Natural Language Processing pipelines address this through fully supervised Named Entity Recognition and Relation Extraction, but require expensive manual annotation and suffer cascading failures when upstream entities are missed. In this study, we developed a zero-shot, agentic workflow, and evaluated five open-source generative Large Language Models (LLMs) to populate 13 College of American Pathologists synoptic fields from lung resection pathology reports. We compared them against a state-of-the-art supervised GatorTron NER-RE baseline using a novel, registry-aligned evaluation framework. The baseline achieved Micro-F1of 0.960, while the best zero-shot model (GPT-OSS-20B) achieved Micro-F1 of 0.893 (recall: 0.949), accurately extracting complex relations like Pathologic Stage without task-specific training. These results suggest that open-source, zero-shot agentic LLMs are a low-cost solution for extracting lung pathology information.
| Comments: | 7 pages, 2 figures, 3 tables. Affiliations: (1) Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA; (2) Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA; (3) College of Nursing, Florida State University, Tallahassee, FL, USA |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.19852 [cs.CL] |
| (or arXiv:2606.19852v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19852
arXiv-issued DOI via DataCite (pending registration)
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