Large Language Model Use and Training Needs Among Rheumatologists: An APLAR Young Rheumatologists International Survey

Aim: Currently, global rheumatology training programs lack structured frameworks to address the digital literacy and governance of large language models (LLMs) use. This study aimed to assess the use and educational gap related to LLMs in rheumatology. Methods: The Asia-Pacific League of Associations for Rheumatology (APLAR) Young Rheumatologists (AYR) conducted an international cross-sectional survey to assess the familiarity, usage patterns, perceptions, and educational unmet needs regarding LLMs among rheumatologists and trainees. Results: A total of 767 participants completed the survey, with 89.6% reporting prior use of LLMs. ChatGPT was the most adopted tool (68.7%), followed by DeepSeek (37.2%) and Google Gemini (21.9%), with a subset of respondents integrating multiple LLMs platforms. While usage is widespread, only 9.9% reported established institutional policies on LLMs use. LLMs users were significantly younger and expressed higher confidence in verifying LLMs-generated information compared to nonusers. Although both groups shared concerns regarding the potential loss of traditional clinical skills (81.1%), active users significantly favored the integration of mandatory LLMs training into core rheumatology curricula. Conclusion: There is a significant disparity between the high prevalence of LLMs adoption in rheumatology and the absence of formal educational guidelines or institutional governance. These findings underscore an urgent need for the development of competency frameworks to ensure the safe, critical, and effective application of LLMs in clinical practice.