Retrieval-augmented generation (RAG) is a method of generating natural language that leverages external knowledge sources, such as large-scale text corpora. RAG first retrieves a set of relevant documents for a given input query or context and then uses these documents as additional input for a neural language model that generates the output text. RAG aims to improve the factual accuracy, diversity, and informativeness of the generated text by incorporating knowledge from the retrieved documents. 

Why is RAG not suitable for all Generative AI applications in healthcare?
Image credit: Nomen4Omen with relabelling by Felix QW, CC BY-SA 3.0 DE, via Wikimedia Commons

However, it may not be suitable for all healthcare applications because of the following reasons: 

RAG relies on the quality and relevance of the retrieved documents, which may not always be available or accurate for specific healthcare domains or tasks. For example, if the task is to generate a personalized treatment plan for a patient based on their medical history and symptoms, RAG may not be able to retrieve any relevant documents from a general-domain corpus, or it may retrieve outdated or inaccurate information that could harm the patient’s health. 

– RAG may not be able to capture the complex and nuanced context of healthcare scenarios, such as the patient’s preferences, values, goals, emotions, or social factors. These aspects may not be explicitly stated in the retrieved documents, or they may require additional knowledge and reasoning to infer. For example, if the task is to generate empathetic and supportive messages for a patient who is diagnosed with a terminal illness, RAG may not be able to consider the patient’s psychological state, coping strategies, or family situation, and may generate generic or inappropriate responses that could worsen the patient’s distress.

– RAG cannot be used to summarize a patient’s medical history as it may not be able to extract the most relevant and important information from the retrieved documents, which may contain a lot of noise, redundancy, or inconsistency. For example, if the task is to generate a concise summary of a patient’s chronic conditions, medications, allergies, and surgeries, RAG may not be able to filter out irrelevant or outdated information, such as the patient’s demographics, vital signs, test results, or minor complaints, or it may include conflicting or duplicate information from different sources. This could lead to a confusing or inaccurate summary that could misinform the patient or the healthcare provider. 

Therefore, RAG is not suitable for all Generative AI applications in healthcare, and it may require careful design, evaluation, and adaptation to ensure its safety, reliability, and effectiveness in specific healthcare contexts and tasks.