Benchmarking large language models for replication of guideline-based PGx recommendations

PGx testing can markedly improve treatment safety and efficacy, yet adoption in health systems remains slow. Our latest peer‑reviewed study—now published in The Pharmacogenomics Journal (Nature Portfolio)—benchmarks how well large language models (LLMs) reproduce guideline‑based pharmacogenomic (PGx) recommendations and outlines a practical path from static, one‑time advice to dynamic guidance updated in real time. Nature

Key findings

General‑purpose models often produced incomplete or unsafe outputs when asked for guideline‑conformant PGx recommendations.

Domain adaptation (including targeted fine‑tuning and structured prompting) substantially improves accuracy and reduces latency, making PGx‑specific LLMs more suitable for clinical decision support.

Why this matters LLMs purpose‑built for PGx can help close the gap between genetic results and bedside actionability—supporting safer, faster, and more consistent medication decisions across diverse patient populations.

Looking ahead We have already released a closed model, Deneb, capable of generating recommendations in real time. Comparative testing against the latest versions (o3, o3-pro, grok-4, claude-4, etc.) will be shared soon.