mlsanityData to 7 Oct 2026

Retrieval-Augmented Generation Assistant for Anatomical Pathology Laboratories

arXiv 2602.22216 ↗First posted 8 Dec 2025cs.IR cs.AIcited by 0

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

Abstract (arXiv)

Accurate and efficient access to laboratory protocols is essential in Anatomical Pathology (AP), where up to 70% of medical decisions depend on laboratory diagnoses. However, static documentation such as printed manuals or PDFs is often outdated, fragmented, and difficult to search, creating risks of workflow errors and diagnostic delays. This study proposes and evaluates a Retrieval-Augmented Generation (RAG) assistant tailored to AP laboratories, designed to provide technicians with context-grounded answers to protocol-related queries. We curated a novel corpus of 99 AP protocols from a Portuguese healthcare institution and constructed 323 question-answer pairs for systematic evaluation. Ten experiments were conducted, varying chunking strategies, retrieval methods, and embedding models. Performance was assessed using the RAGAS framework (faithfulness, answer relevance, context recall) alongside top-k retrieval metrics. Results show that recursive chunking and hybrid retrieval delivered the strongest baseline performance. Incorporating a biomedical-specific embedding model (MedEmbed) further improved answer relevance (0.74), faithfulness (0.70), and context recall (0.77), showing the importance of domain-specialised embeddings. Top-k analysis revealed that retrieving a single top-ranked chunk (k=1) maximized efficiency and accuracy, reflecting the modular structure of AP protocols. These findings highlight critical design considerations for deploying RAG systems in healthcare and demonstrate their potential to transform static documentation into dynamic, reliable knowledge assistants, thus improving laboratory workflow efficiency and supporting patient safety.

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 13 of its 39 references that are in the map. First posted on arXiv 8 Dec 2025.

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13 of 39 references.

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  1. 2025MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot6 Feb

Other references · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2023A Survey of Large Language Modelscited by 2,537
  6. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  7. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  8. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Modelscited by 272
  9. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 104
  10. 2024Is Semantic Chunking Worth the Computational Cost?cited by 43
  11. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generatorcited by 34
  12. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 30

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  1. 2025YpathRAG:A Retrieval-Augmented Generation Framework and Benchmark for Pathology
  2. 2026When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
  3. 2026Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study
  4. 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
  5. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  6. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  7. 2025ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
  8. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights