Retrieval Augmented Large Language Model System for Comprehensive Drug Contraindications
On the map: Retrieval-augmented generation → Medical and biomedical RAG
Abstract (arXiv)
The versatility of large language models (LLMs) has been explored across various sectors, but their application in healthcare poses challenges, particularly in the domain of pharmaceutical contraindications where accurate and reliable information is required. This study enhances the capability of LLMs to address contraindications effectively by implementing a Retrieval Augmented Generation (RAG) pipeline. Utilizing OpenAI's GPT-4o-mini as the base model, and the text-embedding-3-small model for embeddings, our approach integrates Langchain to orchestrate a hybrid retrieval system with re-ranking. This system leverages Drug Utilization Review (DUR) data from public databases, focusing on contraindications for specific age groups, pregnancy, and concomitant drug use. The dataset includes 300 question-answer pairs across three categories, with baseline model accuracy ranging from 0.49 to 0.57. Post-integration of the RAG pipeline, we observed a significant improvement in model accuracy, achieving rates of 0.94, 0.87, and 0.89 for contraindications related to age groups, pregnancy, and concomitant drug use, respectively. The results indicate that augmenting LLMs with a RAG framework can substantially reduce uncertainty in prescription and drug intake decisions by providing more precise and reliable drug contraindication information.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 31 references that are in the map. First posted on arXiv 8 Aug 2025.
Builds on
11 of 31 references.
Closest ancestors · 1
Same direction, newest first
Other references · 10
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
- 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
- 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
- 2025MedGemma Technical Reportcited by 436
- 2023A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challengecited by 55
- 2024Survey of different Large Language Model Architectures: Trends, Benchmarks, and Challengescited by 32
- 2024LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Modelscited by 32
- 2025A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignmentcited by 20
- 2024MedLM: Exploring Language Models for Medical Question Answering Systemscited by 6
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025RAG-based Architectures for Drug Side Effect Retrieval in LLMs
- 2025DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline
- 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
- 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
- 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
- 2025Contradictions in Context: Challenges for Retrieval-Augmented Generation in Healthcare
- 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
- 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights