mlsanityData to 7 Oct 2026

Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

arXiv 2406.12449 ↗First posted 18 Jun 2024cs.AIcited by 3

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

Abstract (arXiv)

Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 37 references that are in the map. First posted on arXiv 18 Jun 2024.

Builds on

11 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Almanac: Retrieval-Augmented Language Models for Clinical Medicine1 Mar

Other references · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Explainability for Large Language Models: A Surveycited by 331
  3. 2024Capabilities of Gemini Models in Medicinecited by 231
  4. 2022Can large language models reason about medical questions?cited by 194
  5. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 175
  6. 2024Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigationcited by 66
  7. 2023Post Hoc Explanations of Language Models Can Improve Language Modelscited by 50
  8. 2024KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniquescited by 37
  9. 2024Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Biascited by 20
  10. 2024Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Biascited by 15

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanicscited by 1
  2. 2025Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatologycited by 0
  3. 2026Mind Companion: An Embodied Conversational Agent for Process-Based Psychotherapycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Pursuing Best Industrial Practices for Retrieval-Augmented Generation in the Medical Domain
  2. 2025Retrieval-Augmented Generation in Industry: An Interview Study on Use Cases, Requirements, Challenges, and Evaluation
  3. 2025Retrieval Augmented Generation Evaluation for Health Documents
  4. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  5. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey
  6. 2025Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
  7. 2024Deploying Large Language Models With Retrieval Augmented Generation
  8. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report