ArticleTherapeutic advances in musculoskeletal disease2026
Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.
Article in Therapeutic advances in musculoskeletal disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) has the capacity to optimise the diagnosis and assessment of damage in inflammatory arthritis. The validation and performance metrics of AI algorithms should be interrogated in order to meaningfully map the existing data and identify evidence gaps. Objectives: To review the literature examining the use of AI algorithms to diagnose and assess damage in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), using plain radiography (XR), ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI). Design: A scoping review was performed for relevant English-language full-text articles or conference abstracts published prior to 12 September 2024 in the EMBASE, PubMed and Web of Science electronic databases. Methods: Abstract, full-text review and data extraction were completed by two authors independently utilising the Covidence platform. Data extracted included the study aims, publication type, study design, imaging modality, joints assessed, patient selection, diagnoses, algorithm(s) used, modifications and techniques utilised to improve model performance, testing and validation methodology and algorithm performance metrics. The review was completed in adherence to the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. Results: Two thousand and sixteen abstracts were screened following removal of 497 duplicates. Forty studies were included for data extraction, four of which were conference abstracts. A majority of studies were published in the last decade (88%) and a minority of studies involved patients with PsA (12.5%). Most studies utilised XR ( Conclusion: There is a paucity of data in the use of AI algorithms for the imaging assessment of PsA compared to RA. In RA and PsA, only a minority of published studies have utilised robust training, validation and testing methodologies in algorithm development. There was significant heterogeneity in the study populations, study design and performance metrics used, which limits comparisons between algorithms and the synthesis of results. Trial registration: Registration was not required in line with the PRISMA-ScR guidelines.
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Registered trials
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.