Artificial intelligence for autoimmune diseases

This is the authors' abstract. We don't add key points for this paper.

Research suggests nearly one in ten individuals is affected by an autoimmune or rheumatic disease – conditions that largely lack definitive cures and show a rapidly rising incidence 1 , 2 . In light of these pressing challenges, recent npj Digital Medicine articles highlight how recent developments in artificial intelligence, particularly in generative artificial intelligence (GenAI), represent a potential paradigm shift in autoimmune and rheumatic disease care 3 , 4 , 5 . Traditional AI applications for rheumatic diseases have mainly focused on narrow tasks, like classification or prediction 6 . In contrast, generative models, such as large language models (LLMs), can produce contextualized clinical content and recommendations informed by large medical datasets, supporting a wide range of clinical tasks 6 , 7 . These models may be particularly valuable for rheumatologic practice, which is often defined by diagnostic uncertainty, varied disease presentations, and the need for individualized treatment 8 , 9 . Despite this alignment, the applications of this technology across autoimmune and rheumatologic clinical care, research, and administrative workflows remain largely unexplored.