The healthcare chatbot market is expected to grow significantly in the coming years, projected to skyrocket from approximately $1.5 billion in 2025 to more than $10 billion by 2034 [1]. These advanced digital assistants are set to transform healthcare accessibility, enabling individuals to get trustworthy information at any time and any location.

But what drives the need for these smart healthcare assistants, and what technologies are unlocking their potential?

Patients and healthcare professionals are both facing daily significant difficulties that specialized chatbots aim to solve. Patients often feel they have limited time with their doctors and seek to be more active in their healthcare journey. They also struggle to find reliable online information for their pathology, leading to increased anxiety. On the other hand, healthcare professionals can spend a lot of time answering repetitive questions, while they could do more critical tasks. A key challenge for the doctor, for instance, is tracking medication adherence – the degree to which a patient follows their drug prescription.

In theory, traditional chatbots could handle many of these issues. Unfortunately, even with the instant information provided by these chatbots, the too generic or incorrect responses are undermining their effectiveness and reliability, which is crucial in healthcare. This article will explore how the Retrieval Augmented Generation (RAG) approach works and why it creates more reliability.

What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation (RAG) enhances chatbot accuracy by providing a relevant context for every user query. In other words, the chatbot will retrieve the most accurate and relevant information from a specialized knowledge base, i.e. a medical library directly accessible by the chatbot. Only then, the chatbot will generate a response, by using the retrieved information. 

There are several upsides of using RAG for healthcare chatbots. First, it mitigates Large Language Model (LLM) hallucinations (when a LLM makes a response up) by using verified knowledge bases, which are crucial for patient safety. With RAG, it is also easy and fast to update the knowledge base with the latest medical guidelines and research, while avoiding to re-train a LLM. Furthermore, RAG adds the ability to source information for each message sent to the patients, providing high transparency and building trust.

RAG-powered healthcare chatbot is built upon several core components:. 

1. Chat Interface: This is where the patient interacts.
2. Knowledge Base: This is the medical library. It has been written by healthcare professionals, and consists of several documents for different medical aspects (such as verified clinical guidelines, drug information, medical articles). Every document has been transformed into numerical representation (embeddings) and stored in a vector database.
3. Embedding Model and Retriever Module: When a patient asks a question, the embedding model transforms it to an embedding. The retriever then finds the most relevant documents within the knowledge base, based on the similarity between the query embeddings and the document embeddings.
4. LLM and Response Formatter: The retrieved documents are passed alongside the initial patient query to a LLM. The LLM will summarize this context to a concise, accurate and comprehensible answer. The response formatter will structure the answer and references the original documents from the knowledge base.

Figure 1. High-level schematic representation of the RAG process [2]

Ethical Challenges

Ethical considerations and specific challenges must be considered when building a RAG powered chatbot in a real world healthcare environment.

First of all, the knowledge base must be high quality and unbiased since the accuracy of RAG depends entirely on the quality of its data. Biased data can lead to inaccurate advice from the chatbot and lead to harmful patient behavior. Ensuring the knowledge base is high quality and free of bias requires a rigorous data governance including version control of the knowledge base and the involvement of medical experts.

We also should care about patient safety and make sure about the responsible use of the chatbot. There is a risk of patients misinterpreting or over-relying on the chatbot’s advice. To mitigate this situation, the chatbot must display clear disclaimers stating that the chatbot does not replace a doctor and human professionals should be contacted in critical situations. 

Final Words

RAG powered chatbots represent a significant leap forward in intelligent healthcare support. They will empower patients with highly available, immediate and reliable information, increasing their medication adherence. They will reduce the time spent on repetitive tasks for medical professionals, enabling them to do more critical tasks. 

In the long term, these chatbots can contribute to democratizing healthcare delivery by reaching the whole population.

References [1] https://www.precedenceresearch.com/healthcare-chatbots-market ; [2] https://www.griddynamics.com/blog/retrieval-augmented-generation-llm