AI for imaging evaluation in rheumatology: applications of radiomics and computer vision—current status, future prospects and potential challenges
Bibliographic record
Abstract
Inflammatory rheumatic diseases, a diverse group of immune-mediated conditions, are characterized by chronic inflammation that can lead to irreversible damage to joints, bones and organs, posing a significant global health challenge. If left untreated, these conditions can severely deteriorate patients' quality of life, underscoring the importance of timely and accurate diagnosis and appropriate management. Artificial intelligence (AI), including radiomics and computer vision, presents promising advancements in improving the early diagnosis and monitoring of these diseases through the analysis of various imaging modalities such as X-rays, CT scans, MRIs and ultrasounds. This review examines the current state of AI applications in the imaging analysis of inflammatory rheumatic diseases, including RA, SpA, SS, SSc and GCA. AI has demonstrated encouraging results, achieving high sensitivity, specificity and accuracy, often on par with or exceeding expert performance. The review also highlights future opportunities for improving the diagnosis and management of rheumatic diseases, as well as the challenges associated with their clinical implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".