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Abstract Large language models (LLMs) like GPT‐4 and Claude are catalyzing transformation across medical research including rheumatology. This review examines their applications, highlighting the pivotal role of prompt engineering in effectively guiding LLMs. Key aspects explored include literature synthesis, data analysis, manuscript drafting, coding assistance, privacy considerations, and generative artificial intelligence integrations. While LLMs accelerate workflows, reliance without apt prompting jeopardizes accuracy. By methodically constructing prompts and gauging model outputs, researchers can maximize relevance and utility. Locally run open‐source models also offer data privacy protections. As LLMs permeate rheumatology research, developing expertise in strategic prompting and assessing model limitations is critical. With proper oversight, LLMs markedly boost scholarly productivity.
Venerito et al. (Wed,) studied this question.