Evidence map›Paper›PMID 42623131›Full record

ArticleRheumatology (Oxford, England)2026

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

Hansel Canagarajah, Pratyasha Saha, Jordan Tsigarides, Nicholas Fuggle, Meghna Jani

Abstract read
In one paragraph

Article in Rheumatology (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hansel CanagarajahInstitute of Inflammation and Aging, University of Birmingham, Birmingham, UK.ORCID 0000-0002-9828-1546
Pratyasha SahaNorwich Medical School, University of East Anglia, Norwich, UK.ORCID 0000-0003-4212-5875
Jordan TsigaridesNorwich Medical School, University of East Anglia, Norwich, UK.ORCID 0000-0001-9893-8002
Nicholas FuggleMRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, UK.ORCID 0000-0001-5463-2255
Meghna JaniCentre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK.ORCID 0000-0002-1487-277X

Funding

Academic Clinical FellowshipNational Health Service or the UK Department of Health and Social CareNational Institute for Health and Care Research (NIHR) Advanced Fellowship NIHR301413NIHRSpecialised Foundation
6 · The paper itself

Abstract

objectivesArtificial 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.

methodsA 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.

resultsOf 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%).

conclusionAI 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.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelHealth Knowledge, Attitudes, PracticeRheumatologyHumansSurveys and QuestionnairesUnited Kingdomambient scribesartificial intelligencedata privacydigital readinessmachine learning

Identifiers

PMID42623131
PMCPMC13584210

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.