Perceptions, knowledge and adoption of artificial intelligence in rheumatology: results from a British Society for Rheumatology survey

Abstract

Abstract Objectives Artificial intelligence (AI) and machine learning applications are rapidly expanding across healthcare. Successful implementation of AI technologies in rheumatology will depend not only on technical performance but also on the perceptions and preparedness of end-users. This study evaluated the current opinions, expectations and concerns about AI among healthcare professionals and researchers in rheumatology across the UK. Methods A 19-item survey was designed and distributed through national and regional networks aimed at the rheumatology workforce between June 2025 and January 2026, targeted at consultant rheumatologists, doctors-in-training, allied health professionals, specialist nurses and non-clinical researchers in rheumatology. The questions included respondent background data, current applications of AI in clinical care and research, opinions about AI in terms of perceived impact, concerns, educational needs and expected performance. Results Of the 218 respondents, 39 to40% reported daily or weekly use of AI in research and clinical practice respectively. The most common clinical uses were using large language models (LLMs) to look up medical facts (45%), to improve grammar/spelling of clinical documentation (28%), to generate differential diagnoses (22%) and ambient scribe use (17%). Eighty-six percent anticipated that AI would substantially impact clinical practice in 5 years or less. Administrative tasks (85%) and musculoskeletal imaging (63%) were perceived as the areas likely to experience the greatest impact from AI. Highlighted concerns included data security/privacy (70%) and medical liability (70%), followed by lack of explainability (47%). One in four reported excellent confidence in using digital technology, with only 6% self-rating their AI knowledge as excellent. A strong interest in education about AI was expressed regarding several areas including the ethical and safe use of AI (66%), safe and efficient use of LLMs in clinical practice (64%), and use of ambient AI scribes (59%). Conclusion AI is already being used frequently in UK rheumatology practice and research, with most anticipating a considerable impact on clinical care within the next 5 years or less. However, despite enthusiasm for adoption, important concerns regarding data security, liability and explainability remain, alongside low self-reported AI knowledge, highlighting the need for targeted education, robust governance and safe clinical implementation strategies.