mlsanityData to 7 Oct 2026

Lab-AI: Using Retrieval Augmentation to Enhance Language Models for Personalized Lab Test Interpretation in Clinical Medicine

arXiv 2409.18986 ↗First posted 16 Sep 2024cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring conditional factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ranges using retrieval-augmented generation (RAG) from credible health sources. Lab-AI has two modules: factor retrieval and normal range retrieval. We tested these on 122 lab tests: 40 with conditional factors and 82 without. For tests with factors, normal ranges depend on patient-specific information. Our results show GPT-4-turbo with RAG achieved a 0.948 F1 score for factor retrieval and 0.995 accuracy for normal range retrieval. GPT-4-turbo with RAG outperformed the best non-RAG system by 33.5% in factor retrieval and showed 132% and 100% improvements in question-level and lab-level performance, respectively, for normal range retrieval. These findings highlight Lab-AI's potential to enhance patient understanding of lab results.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 6 of its 27 references that are in the map. First posted on arXiv 16 Sep 2024.

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6 of 27 references.

References · 6

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  3. 2024Large Language Models: A Surveycited by 506
  4. 2023A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challengecited by 55
  5. 2024LongEmbed: Extending Embedding Models for Long Context Retrievalcited by 47
  6. 2024Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Studycited by 6

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  1. 2026MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine
  2. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  3. 2024Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
  4. 2026A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench
  5. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines
  6. 2025ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
  7. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  8. 2025ASTRID -- An Automated and Scalable TRIaD for the Evaluation of RAG-based Clinical Question Answering Systems