Language

English

Publication Date

5-20-2024

Journal

Journal of the American Medical Informatics Association

DOI

10.1093/jamia/ocae039

PMID

38447590

PMCID

PMC11105140

PubMedCentral® Posted Date

3-6-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Objective: This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics and to enhance patient care with equitable access.

Materials and methods: The AI assistant employs retrieval-augmented generation (RAG), which combines retrieval and generative techniques, by harnessing a knowledge base (KB) that comprises data from the Clinical Pharmacogenetics Implementation Consortium (CPIC). It uses context-aware GPT-4 to generate tailored responses to user queries from this KB, further refined through prompt engineering and guardrails.

Results: Evaluated against a specialized PGx question catalog, the AI assistant showed high efficacy in addressing user queries. Compared with OpenAI's ChatGPT 3.5, it demonstrated better performance, especially in provider-specific queries requiring specialized data and citations. Key areas for improvement include enhancing accuracy, relevancy, and representative language in responses.

Discussion: The integration of context-aware GPT-4 with RAG significantly enhanced the AI assistant's utility. RAG's ability to incorporate domain-specific CPIC data, including recent literature, proved beneficial. Challenges persist, such as the need for specialized genetic/PGx models to improve accuracy and relevancy and addressing ethical, regulatory, and safety concerns.

Conclusion: This study underscores generative AI's potential for transforming healthcare provider support and patient accessibility to complex pharmacogenomic information. While careful implementation of large language models like GPT-4 is necessary, it is clear that they can substantially improve understanding of pharmacogenomic data. With further development, these tools could augment healthcare expertise, provider productivity, and the delivery of equitable, patient-centered healthcare services.

Keywords

Humans, Precision Medicine, Pharmacogenetics, Artificial Intelligence, Knowledge Bases, Information Storage and Retrieval, Pharmacogenomic Testing, generative AI, pharmacogenomic testing, AI assistant, retrieval-augmented generation, large language models, OpenAI GPT-4

Published Open-Access

yes

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