Retrieval-Augmented Generation: Advancing personalized care and research in oncology
Abstract
Retrieval-Augmented Generation (RAG) pairs large language models (LLMs) with recent data to produce more accurate, context-aware outputs. By converting text into numeric embeddings, RAG locates and retrieves relevant "chunks" of data, that along with the query, ground the model's responses in current, specific information. This process helps reduce outdated or fabricated answers. In oncology, RAG has shown particular promise. Studies have demonstrated its ability to improve treatment recommendations by integrating genetic profiles, strengthened clinical trial matching through biomarker analysis, and accelerated drug development by clarifying model-driven insights. Despite its advantages, RAG depends on high-quality data. Biased or incomplete sources can lead to inaccurate outcomes. Careful implementation and human oversight are crucial for ensuring the effectiveness and reliability of RAG in oncology.
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RAG enables precise clinical trial matching by aligning patient profiles with the eligibility criteria extracted from trial registries.
“RAG enables precise clinical trial matching by aligning patient profiles with the eligibility criteria extracted from trial registries [66].”
From Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review (2025) · cites this paper as [66]
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- Authors
- Zarfati, M., Soffer, S., Nadkarni, G. N., & Klang, E.
- Affiliations
- Hebrew University of Jerusalem (IL)
- Tel Aviv University (IL)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Klang, Eyal
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 220
- Pages
- 115341
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W4408184919
- Cited by
- 20
- References
- 9
- Citations by year
- 2026: 10 · 2025: 10
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Cancer (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- retrieval-augmented generation, large language models, oncology, precision oncology, treatment guidelines, genetic profile, clinical trial matching, biomarker analysis, drug development, data quality, training data bias, human oversight
- MeSH terms
- Humans, Medical Oncology, Neoplasms, Information Storage and Retrieval, Biomedical Research, Precision Medicine
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