Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
This is an incremental approach targeting medical AI applications to improve generative model performance.
The paper addresses limitations in generative AI for medicine by proposing retrieval-augmented generation (RAG) as a solution to improve content accuracy through external knowledge retrieval, aiming to enhance equity, reliability, and personalization in healthcare.
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.