Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models...19th Health Informatics Meets Digital Health Conference (dHealth), May 6-7, 2025, Vienna, Austria.
Background: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-prone. Objectives: This study evaluates the effectiveness of large language models (LLMs) in classifying malignancies and detecting tumor types from pat...
| Publicado en: | Studies in Health Technology & Informatics Vol. 324; pp. 270 - 276 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184908405&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184908405 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 324 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 184908405 184908405 184908405 10.3233/SHTI250200 184908405 ppf: 270 ppct: 6 formats: tig: atl: Enhancing Malignancy Detection and Tumor Classification in Pathology Reports: A Comparative Evaluation of Large Language Models...19th Health Informatics Meets Digital Health Conference (dHealth), May 6-7, 2025, Vienna, Austria. aug: au: NEURURER, Sabrina B. TAHA, Hasan MUEHLBOECK, Helmut HICKMANN, Christoph GSCHEIDLINGER, Patricia RICHTER, Stefan DANLER, Martin HACKL, Werner O. UEBEREGGER, Marko SCHWEITZER, Marco PFEIFER, Bernhard affil: Department of Clinical Epidemiology, Tirol Kliniken GmbH, Innsbruck, Tirol, Austria sug: subj: Natural Language Processing Artificial Intelligence Neoplasms Classification Neoplasm Grading Classification Neural Networks (Computer) Neoplasms Pathology Medical Informatics Congresses and Conferences Austria Austria Human Sensitivity and Specificity Early Detection of Cancer Algorithms Registries, Disease Decision Support Systems, Clinical Electronic Health Records Data Collection Comparative Studies Information Technology ab: Background: Cancer registries require accurate and efficient documentation of malignancies, yet current manual methods are time-consuming and error-prone. Objectives: This study evaluates the effectiveness of large language models (LLMs) in classifying malignancies and detecting tumor types from pathology reports. Methods: Using a synthetic dataset of 227 reports, the performance of four LLMs and a score-based algorithm was compared against expert-labeled gold standards. Results: The LLMs, particularly GPT-4o and Llama3.3, demonstrated high sensitivity and specificity in both malignancy detection and tumor classification, significantly outperforming traditional algorithms. Conclusion: LLMs enhance the accuracy and efficiency of cancer data classification and hold promise for improving public health monitoring and clinical decision-making. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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