From chat to act: large language model agents and agentic AI as the next frontier of AI in rheumatology

Large language models (LLMs) have begun to influence rheumatology, yet their static knowledge and hallucination risks limit their potential. Retrieval-augmented generation mitigates some limitations, but complex rheumatologic care demands real-time data access, multistep reasoning, and tool usage that exceed standard LLM capabilities. The objective of this study is to explore how agentic artificial intelligence (AI) can address the limitations of current LLM applications in rheumatology.

We conducted a viewpoint analysis of the capabilities of agentic AI systems, focusing on their technical foundations, current use cases in healthcare, and relevance to the specific demands of rheumatologic care.

Agentic AI extends LLMs with planning, memory, and the ability to interact with external tools, enabling execution of complex tasks. These capabilities offer promising applications in rheumatology, including personalized treatment planning, automated literature synthesis, and clinical decision support.

Agentic AI systems represent a necessary evolution to meet the complexity of rheumatologic care. Regulatory, ethical and technical challenges must be overcome before agentic systems can be safely deployed in routine rheumatologic care.