August 7, 2025Open Access

Large Language Models for Detection of Genetic Variants in Biomedical Literature

Key Points

MAIN FINDING: Large language models can effectively extract genetic variants from biomedical literature with high precision and recall.
KEY EVIDENCE: LLaMA-3.3-70b achieved 98% precision, 92% recall, and 99% accuracy, outperforming conventional methods.
APPROACH: The study assessed four large language models, focusing on their performance in prostate cancer literature.
SIGNIFICANCE: These findings highlight the potential for automating genomic variant extraction, necessary for advancing personalized medicine.

Abstract

Personalized medicine relies on understanding genetic variations, but systematically tracking literature linking these variations to clinical outcomes remains challenging. This study assessed the feasibility and performance of large language models (LLMs) in extracting genes and variants from prostate cancer literature. Four LLMs, including LLaMA 3.3, GPT-4o, and DeepSeek-V3, were evaluated, with LLaMA-3.3-70b demonstrating the highest overall performance (98% precision, 92% recall, 99% accuracy). In contrast, conventional information extraction methods had poor recall and high false positive rates. Moreover, LLMs inferred contextual details, offering enriched insights but occasionally introducing unsupported information. These findings demonstrate the promise of LLMs in automating genomic variant extraction, while also highlighting the need for rigorous validation and careful integration into clinical and research workflows.

Large Language Models for Detection of Genetic Variants in Biomedical Literature

Key Points

Abstract

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