mlsanityData to 14 Oct 2026

Optimizing Retrieval-Augmented Generation of Medical Content for Spaced Repetition Learning

arXiv 2503.01859 ↗First posted 23 Feb 2025cs.CL cs.AI cs.IRcited by 0

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

Abstract (arXiv)

Advances in Large Language Models revolutionized medical education by enabling scalable and efficient learning solutions. This paper presents a pipeline employing Retrieval-Augmented Generation (RAG) system to prepare comments generation for Poland's State Specialization Examination (PES) based on verified resources. The system integrates these generated comments and source documents with a spaced repetition learning algorithm to enhance knowledge retention while minimizing cognitive overload. By employing a refined retrieval system, query rephraser, and an advanced reranker, our modified RAG solution promotes accuracy more than efficiency. Rigorous evaluation by medical annotators demonstrates improvements in key metrics such as document relevance, credibility, and logical coherence of generated content, proven by a series of experiments presented in the paper. This study highlights the potential of RAG systems to provide scalable, high-quality, and individualized educational resources, addressing non-English speaking users.

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 9 of its 31 references that are in the map. First posted on arXiv 23 Feb 2025.

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9 of 31 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  3. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  4. 2024BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domainscited by 301
  5. 2024Capabilities of Gemini Models in Medicinecited by 237
  6. 2025Optimization Learningcited by 15
  7. 2024GPT-4 passes most of the 297 written Polish Board Certification Examinationscited by 10
  8. 2024Polish-English medical knowledge transfer: A new benchmark and resultscited by 8
  9. 2024Assessing generalization capability of text ranking models in Polishcited by 3

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  1. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  2. 2023Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education
  3. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  4. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  5. 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
  6. 2024Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
  7. 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
  8. 2024Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering