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Leveraging Large Language Models for Cancer Variant Classification: A Comparative Study of GPT-4o, LLaMA 3, and Qwen

Kuan-Hsun Lin^1,2, Paul Chih-Hsueh Chen^3,4, Chen-Tsung Kuo^1,2

¹Department of Information Management, Taipei Veterans General Hospital, Taipei, 11267, Taiwan, R.O.C.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

This study evaluates large language models (LLMs) like GPT-4o for classifying cancer genomic variants. LLMs show promise for improving precision oncology interpretation of sequencing data.

Keywords:

CIViC Cancer Variant Classification Clinical Genomics Genomic Profiling Large Language Models OncoKB Precision Oncology

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Area of Science:

Genomics
Computational Biology
Artificial Intelligence

Background:

Interpreting cancer genomic variants is crucial for precision oncology.
Large language models (LLMs) offer potential for automating variant classification.
Current LLM capabilities for clinical genomic interpretation require systematic evaluation.

Purpose of the Study:

To benchmark the performance of state-of-the-art LLMs in classifying cancer genomic variants.
To assess the utility of GPT-4o, LLaMA 3, and Qwen 2.5 for clinical genomic interpretation.
To provide insights into the application of AI in precision oncology.

Main Methods:

Curated cancer variant databases were used for benchmarking.
Three advanced LLMs (GPT-4o, LLaMA 3, Qwen 2.5) were evaluated.
Variant classification accuracy and clinical utility were assessed.

Main Results:

The study systematically compared the performance of GPT-4o, LLaMA 3, and Qwen 2.5.
Results indicate the potential and limitations of current LLMs for genomic variant interpretation.
Specific performance metrics for each LLM were detailed.

Conclusions:

LLMs demonstrate potential as tools for cancer variant interpretation in precision oncology.
Further development and validation are needed for robust clinical integration.
AI-driven approaches can aid in accelerating genomic data analysis.