*Result*: Leveraging Large Language Models for Cancer Variant Classification: A Comparative Study of GPT-4o, LLaMA 3, and Qwen 2.5.
Title:
Leveraging Large Language Models for Cancer Variant Classification: A Comparative Study of GPT-4o, LLaMA 3, and Qwen 2.5.
Authors:
Lin KH; Department of Information Management, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 11219, Taiwan, R.O.C., Chen PC; Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.; School of Medicine, National Yang Ming Chiao Tung University, 112304, Taipei, Taiwan, R.O.C., Kuo CT; Department of Information Management, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 11219, Taiwan, R.O.C., Chu YC; Department of Information Management, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.; Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, 11219, Taiwan, R.O.C., Yeh YC; Department of Pathology and Laboratory Medicine, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.; School of Medicine, National Yang Ming Chiao Tung University, 112304, Taipei, Taiwan, R.O.C.
Source:
Studies in health technology and informatics [Stud Health Technol Inform] 2025 Aug 07; Vol. 329, pp. 1728-1729.
Publication Type:
Journal Article; Comparative Study
Language:
English
Journal Info:
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991-
MeSH Terms:
Contributed Indexing:
Keywords: CIViC; Cancer Variant Classification; Clinical Genomics; Genomic Profiling; Large Language Models; OncoKB; Precision Oncology
Entry Date(s):
Date Created: 20250808 Date Completed: 20250808 Latest Revision: 20250808
Update Code:
20260130
DOI:
10.3233/SHTI251185
PMID:
40776202
Database:
MEDLINE
*Further Information*
*Background: Interpreting genomic variants from cancer sequencing data is a critical yet complex task in precision oncology. With advances in large language models (LLMs), there is increasing interest in leveraging their capacity for variant classification. This study benchmarks three state-of-the-art LLMs - GPT-4o, LLaMA 3, and Qwen 2.5 - on curated cancer variant databases to assess their utility in clinical genomic interpretation.*