Empowering personalized pharmacogenomics with generative AI solutions

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. Methods The AI assistant employs Retrieval Augmented Generation (RAG) combining retrieval and generative techniques. It employs a Knowledge Base (KB) comprising Clinical Pharmacogenetics Implementation Consortium (CPIC) data, with context-aware GPT-4 generating tailored responses to user queries from this KB, 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. ### Competing Interest Statement Dr. Eric Venner is the cofounder of Codified Genomics. No other authors have any competing interests. ### Funding Statement This work was supported by the National Institutes of Health grant number 1OT2OD002751. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data supporting the findings of this article can be accessed within the article, through the referenced GitHub links, and in the supplementary materials.