Large Language Models for Detection of Genetic Variants in Biomedical Literature

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.