ArticleJournal of the American Medical Informatics Association : JAMIA2024
Empowering personalized pharmacogenomics with generative AI solutions.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- Towards convergence of AI and blockchain for personalized medicine in pharmacogenomics.Scientific reports · 2026Article
- An agentic AI system for automated pharmacogenomic recommendation generation.NPJ digital medicine · 2026Article
- Advancing Nursing Education: Evaluating Artificial Intelligence (AI) and Escape Room (ER) Approaches for E-Learning and Interpretation Skills.Nursing research and practice · 2026Article
- Development of an AI-driven chatbot for medication-assisted treatment standards in Scotland.Frontiers in digital health · 2026Article
- Personalized Perioperative Opioid Strategies in Children: Focus on Methadone, Pharmacogenomics and Prevention of Persistent Postoperative Opioid Use.Children (Basel, Switzerland) · 2025Review
- Artificial intelligence coupled to pharmacometrics modelling to tailor malaria and tuberculosis treatment in Africa.Nature communications · 2025Article
- The Impact of Access to Clinical Guidelines on LLM-Based Treatment Recommendations for Chronic Hepatitis B.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Progress in Pharmacogenomics Implementation in the United States: Barrier Erosion and Remaining Challenges.Clinical pharmacology and therapeutics · 2025Review
- Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine.Mayo Clinic proceedings. Digital health · 2025Review
- Benchmarking large language models for replication of guideline-based PGx recommendations.The pharmacogenomics journal · 2025Article
- Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: Prompt Engineering Project.JMIR cancer · 2025Article
- Retrieval augmented generation for large language models in healthcare: A systematic review.PLOS digital health · 2025Article
- Inferring Drug-Gene Relationships in Cancer Using Literature-Augmented Large Language Models.Cancer research communications · 2025Article
- Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine.Proceedings. IEEE International Conference on Healthcare Informatics · 2025Article
- PGxQA: A Resource for Evaluating LLM Performance for Pharmacogenomic QA Tasks.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review.BMC medical informatics and decision making · 2024Review
- Creating a biomedical knowledge base by addressing GPT inaccurate responses and benchmarking context.bioRxiv : the preprint server for biology · 2024Article
- Engine of Innovation in Hospital Pharmacy: Applications and Reflections of ChatGPT.Journal of medical Internet research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveThis 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
methodsThe 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.
resultsEvaluated 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.
conclusionThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.