The Future of Giant Cell Arteritis Diagnosis and Management: A Systematic Review of Artificial Intelligence and Predictive Analytics
Bibliographic record
Abstract
Giant cell arteritis (GCA), a systemic vasculitis affecting large and medium-sized arteries, poses significant diagnostic and management challenges, particularly in preventing irreversible complications like vision loss. Recent advancements in artificial intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer promising solutions to enhance diagnostic accuracy and optimize treatment strategies for GCA. This systematic review, conducted according to the PRISMA 2020 guidelines, synthesizes existing literature on AI applications in GCA care, with a focus on diagnostic accuracy, treatment outcomes, and predictive modeling. A comprehensive search of databases (MEDLINE (via PubMed), Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science) from their inception to September 2024 identified 309 studies, with four meeting inclusion criteria. The review highlights the potential of AI to improve diagnostic accuracy through image analysis of color Doppler ultrasound and clinical data, with AI models like random forests, convolutional neural networks, and logistic regression demonstrating effectiveness in predicting GCA diagnosis and relapse after glucocorticoid tapering. Despite these promising findings, challenges such as the need for larger datasets, prospective validation, and addressing ethical concerns remain. The review underscores the transformative potential of AI in GCA care while emphasizing the need for further research to refine and validate AI-driven tools for broader 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".