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Large Language Model Use and Training Needs Among Rheumatologists: An APLAR Young Rheumatologists International
Latika Gupta1,2,3, Masatoshi Kanda4, Khandker Mahbub-Uz-Zaman5
1Department of Rheumatology, Royal Wolverhampton Hospitals NHS Trust, Wolverhampton, UK.
International Journal of Rheumatic Diseases
|April 16, 2026
Summary
Rheumatologists widely use large language models (LLMs), but training programs lack digital literacy and governance frameworks. An urgent need exists for competency frameworks to ensure safe and effective LLM integration in rheumatology practice.
Area of Science:
- Medical Education
- Digital Health
- Artificial Intelligence in Medicine
Background:
- Global rheumatology training programs lack structured frameworks for digital literacy and governance of large language models (LLMs).
- Assessing the current landscape of LLM adoption and identifying educational gaps in rheumatology is crucial for future training.
- The rapid advancement of AI necessitates understanding its impact on clinical practice and medical education.
Purpose of the Study:
- To assess the current use, familiarity, and perceptions of LLMs among rheumatologists and trainees.
- To identify educational unmet needs and the gap in digital literacy regarding LLMs in rheumatology.
- To evaluate the need for structured training and governance frameworks for LLM integration.
Main Methods:
- An international cross-sectional survey was conducted among rheumatologists and trainees.
- The survey, administered by the Asia-Pacific League of Associations for Rheumatology (APLAR) Young Rheumatologists (AYR), assessed LLM usage patterns, perceptions, and educational needs.
- Data from 767 participants were analyzed to understand LLM adoption and identify knowledge gaps.
Main Results:
- Nearly 90% of participants reported using LLMs, with ChatGPT being the most common tool.
- Despite widespread adoption, only 9.9% of institutions have established LLM usage policies.
- Younger users showed higher confidence in verifying LLM-generated information, and active users advocated for mandatory LLM training.
Conclusions:
- A significant disparity exists between high LLM adoption and the lack of formal educational guidelines and institutional governance in rheumatology.
- There is an urgent need to develop competency frameworks for the safe, critical, and effective application of LLMs in clinical rheumatology.
- Integrating LLMs into rheumatology curricula is essential to address digital literacy and ensure responsible AI use.
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