ArticleThe pharmacogenomics journal2025
Benchmarking large language models for replication of guideline-based PGx recommendations.
Article in The pharmacogenomics journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The trial behind it
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Who cites it
2 citing papers in PubMed.
- An agentic AI system for automated pharmacogenomic recommendation generation.NPJ digital medicine · 2026Article
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluated the ability of large language models (LLMs) to generate clinically accurate pharmacogenomic (PGx) recommendations aligned with CPIC guidelines. Using a benchmark of 599 curated gene-drug-phenotype scenarios, we compared five leading models, including GPT-4o and fine-tuned LLaMA variants, through both standard lexical metrics and a novel semantic evaluation framework (LLM Score) validated by expert review. General-purpose models frequently produced incomplete or unsafe outputs, while our domain-adapted model achieved superior performance, with an LLM Score of 0.92 and significantly faster inference speed. Results highlight the importance of fine-tuning and structured prompting over model scale alone. This work establishes a robust framework for evaluating PGx-specific LLMs and demonstrates the feasibility of safer, AI-driven personalized medicine.
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Registered trials
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