RAGing ahead in rheumatology: new language model architectures to tame artificial intelligence

Abstract

Suport a la decisió clínica; Grans models lingüístics; Generació augmentada amb recuperació; Reumatologia

Apoyo a la decisión clínica; Grandes modelos lingüísticos; Generación aumentada con recuperación; Reumatología

Clinical decision support; Large language models; Retrieval-augmented generation; Rheumatology

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 trials, enhancing pharmacovigilance efforts, and supporting personalized patient education. RAG also offers advantages in data privacy by enabling local data handling and reducing reliance on large, general-purpose models. Future directions involve integrating RAG with fine-tuned, smaller LLMs and exploring multimodal models that can process diverse data types. Challenges such as infrastructure costs, data privacy concerns, and the need for specialized evaluation metrics must be addressed. Nevertheless, RAG presents a promising opportunity to improve AI applications in rheumatology, offering a more precise, accountable, and sustainable approach to integrating advanced language models into clinical practice and research.