Validating Pharmacogenomics Generative Artificial Intelligence Query Prompts Using Retrieval-Augmented Generation (RAG)

TL;DR

This study evaluates Sherpa Rx, an AI tool using retrieval-augmented generation for pharmacogenomics, demonstrating high accuracy and relevance in generating personalized drug-gene response information, outperforming other models.

Contribution

The paper introduces Sherpa Rx, a novel RAG-based AI system that integrates pharmacogenomics guidelines and databases to improve response accuracy and relevance.

Findings

Sherpa Rx achieved high scores in accuracy, relevance, and clarity.

Incorporating PharmGKB data improved response completeness.

Sherpa Rx outperformed ChatGPT-4omini in accuracy and real-world quiz performance.

Abstract

This study evaluated Sherpa Rx, an artificial intelligence tool leveraging large language models and retrieval-augmented generation (RAG) for pharmacogenomics, to validate its performance on key response metrics. Sherpa Rx integrated Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines with Pharmacogenomics Knowledgebase (PharmGKB) data to generate contextually relevant responses. A dataset (N=260 queries) spanning 26 CPIC guidelines was used to evaluate drug-gene interactions, dosing recommendations, and therapeutic implications. In Phase 1, only CPIC data was embedded. Phase 2 additionally incorporated PharmGKB content. Responses were scored on accuracy, relevance, clarity, completeness (5-point Likert scale), and recall. Wilcoxon signed-rank tests compared accuracy between Phase 1 and Phase 2, and between Phase 2 and ChatGPT-4omini. A 20-question quiz assessed the tool's real-world applicability against other models. In Phase 1 (N=260), Sherpa Rx demonstrated high performance of accuracy 4.9, relevance 5.0, clarity 5.0, completeness 4.8, and recall 0.99. The subset analysis (N=20) showed improvements in accuracy (4.6 vs. 4.4, Phase 2 vs. Phase 1 subset) and completeness (5.0 vs. 4.8). ChatGPT-4omini performed comparably in relevance (5.0) and clarity (4.9) but lagged in accuracy (3.9) and completeness (4.2). Differences in accuracy between Phase 1 and Phase 2 was not statistically significant. However, Phase 2 significantly outperformed ChatGPT-4omini. On the 20-question quiz, Sherpa Rx achieved 90% accuracy, outperforming other models. Integrating additional resources like CPIC and PharmGKB with RAG enhances AI accuracy and performance. This study highlights the transformative potential of generative AI like Sherpa Rx in pharmacogenomics, improving decision-making with accurate, personalized responses.