ReviewFrontiers in immunology2024
Artificial intelligence for predicting treatment responses in autoimmune rheumatic diseases: advancements, challenges, and future perspectives.
Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed.
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Diagnostic Accuracy of Multimodal Large Language Models for Four-Class Benchmark of Oral Autoimmune Blistering Diseases: A Multicenter Paired Study.Diagnostics (Basel, Switzerland) · 2026Article
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Toward causal artificial intelligence for biologic treatment response in rheumatoid arthritis: current evidence and future directions.Rheumatology international · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Inflammation-modulating agents in chronic limb-threatening ischemia: a narrative review of therapeutic potential and future directions.Annals of medicine and surgery (2012) · 2026Article
- Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine.World journal of gastroenterology · 2026Review
- Enhancing the Diagnosis of Behçet's Disease Using Machine Learning: A Comparative Study on Clinical Data From Saudi Arabia.International journal of telemedicine and applications · 2026Article
- Machine Learning-Based Analysis of Serum Interleukin-39 and Interleukin-40 Levels for Differentiating Rheumatoid Arthritis and Systemic Lupus Erythematosus.Medeniyet medical journal · 2025Article
- Concordance Between the Multidisciplinary Team and ChatGPT-4o Decisions: A Blinded, Cross-Sectional Concordance Study in Systemic Autoimmune Rheumatic Diseases.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative evaluation of large language models on multiple-choice and image-based rheumatology questions.Rheumatology international · 2025Article
- Artificial intelligence for autoimmune diseases.NPJ digital medicine · 2025Article
- Diagnostic Value of Machine Learning Models in Inflammation of Unknown Origin.Journal of clinical medicine · 2025Article
- Five years of the Digital Rheumatology Network: insights and future directions.EULAR rheumatology open · 2025Review
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis.Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoimmune rheumatic diseases (ARD) present a significant global health challenge characterized by a rising prevalence. These highly heterogeneous diseases involve complex pathophysiological mechanisms, leading to variable treatment efficacies across individuals. This variability underscores the need for personalized and precise treatment strategies. Traditionally, clinical practices have depended on empirical treatment selection, which often results in delays in effective disease management and can cause irreversible damage to multiple organs. Such delays significantly affect patient quality of life and prognosis. Artificial intelligence (AI) has recently emerged as a transformative tool in rheumatology, offering new insights and methodologies. Current research explores AI's capabilities in diagnosing diseases, stratifying risks, assessing prognoses, and predicting treatment responses in ARD. These developments in AI offer the potential for more precise and targeted treatment strategies, fostering optimism for enhanced patient outcomes. This paper critically reviews the latest AI advancements for predicting treatment responses in ARD, highlights the current state of the art, identifies ongoing challenges, and proposes directions for future research. By capitalizing on AI's capabilities, researchers and clinicians are poised to develop more personalized and effective interventions, improving care and outcomes for patients with ARD.
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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.