Title | Empowering personalized pharmacogenomics with generative AI solutions. |
Publication Type | Journal Article |
Year of Publication | 2024 |
Authors | Murugan, M, Yuan, B, Venner, E, Ballantyne, CM, Robinson, KM, Coons, JC, Wang, L, Empey, PE, Gibbs, RA |
Journal | J Am Med Inform Assoc |
Date Published | 2024 Mar 06 |
ISSN | 1527-974X |
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. |
DOI | 10.1093/jamia/ocae039 |
Alternate Journal | J Am Med Inform Assoc |
PubMed ID | 38447590 |
Grant List | 1OT2OD002751 / NH / NIH HHS / United States |