RAGing ahead in rheumatology: new language model architectures to tame artificial intelligence
Abstract
Artificial intelligence (AI) is increasingly transforming rheumatology with research on disease detection, monitoring, and outcome prediction through the analysis of large datasets. The advent of generative models and large language models (LLMs) has expanded AI's capabilities, particularly in natural language processing (NLP) tasks such as question-answering and medical literature synthesis. While NLP has shown promise in identifying rheumatic diseases from electronic health records with high accuracy, LLMs face significant challenges, including hallucinations and a lack of domain-specific knowledge, which limit their reliability in specialized medical fields like rheumatology. Retrieval-augmented generation (RAG) emerges as a solution to these limitations by integrating LLMs with real-time access to external, domain-specific databases. RAG enhances the accuracy and relevance of AI-generated responses by retrieving pertinent information during the generation process, reducing hallucinations, and improving the trustworthiness of AI applications. This architecture allows for precise, context-aware outputs and can handle unstructured data effectively. Despite its success in other industries, the application of RAG in medicine, and specifically in rheumatology, remains underexplored. Potential applications in rheumatology include retrieving up-to-date clinical guidelines, summarizing complex patient histories from unstructured data, aiding in patient identification for clinical…
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