# Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine

arXiv: https://arxiv.org/abs/2406.12449 · HTML: https://mlsanity.com/paper/2406.12449/

## Key facts

- Cited by 3 papers in the map (data through 14 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.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explainability for Large Language Models: A Survey](https://mlsanity.com/paper/2309.01029/) (2023)
- [Capabilities of Gemini Models in Medicine](https://mlsanity.com/paper/2404.18416/) (2024)
- [Can large language models reason about medical questions?](https://mlsanity.com/paper/2207.08143/) (2022)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [Almanac: Retrieval-Augmented Language Models for Clinical Medicine](https://mlsanity.com/paper/2303.01229/) (2023)
- [Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation](https://mlsanity.com/paper/2404.01030/) (2024)
- [Post Hoc Explanations of Language Models Can Improve Language Models](https://mlsanity.com/paper/2305.11426/) (2023)
- [KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques](https://mlsanity.com/paper/2403.05881/) (2024)
- [Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias](https://mlsanity.com/paper/2401.14589/) (2024)
- [Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias](https://mlsanity.com/paper/2405.05506/) (2024)

## Built on by (most cited first, 3 of 3)

- [Build AI Assistants using Large Language Models and Agents to Enhance the Engineering Education of Biomechanics](https://mlsanity.com/paper/2511.15752/) (2025)
- [Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology](https://mlsanity.com/paper/2507.07983/) (2025)
- [Mind Companion: An Embodied Conversational Agent for Process-Based Psychotherapy](https://mlsanity.com/paper/2606.17789/) (2026)

## 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.