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Summary

Large language models (LLMs) show promise for pharmacogenetics (PGx) information delivery. However, current AI models, including GPT-4o, still provide inaccurate clinical information, hindering widespread adoption in healthcare.

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Area of Science:

Pharmacogenetics
Artificial Intelligence
Precision Medicine

Background:

Pharmacogenetics (PGx) is crucial for precision medicine, yet clinical implementation is slow due to limited clinician and patient education.
Large Language Models (LLMs) offer potential for disseminating PGx information but risk providing hazardous inaccuracies in clinical settings.

Purpose of the Study:

To evaluate the accuracy of LLM-based chatbots in answering pharmacogenetics questions for clinicians, patients, and researchers.
To quantify the performance gap of current LLMs against clinical standards for pharmacogenetics information.

Main Methods:

Developed automated and expert-scored tests to assess chatbot performance on pharmacogenetics queries.
Applied benchmark tests to evaluate state-of-the-art LLMs, including GPT-4o.

Main Results:

Newer LLMs like GPT-4o demonstrate improved performance over predecessors.
Despite advancements, current LLMs do not meet the required accuracy standards for clinical pharmacogenetics applications.

Conclusions:

LLMs show potential for pharmacogenetics education but require further development to ensure clinical safety and reliability.
The developed benchmark serves as a public resource to drive progress in clinical AI for pharmacogenetics.