ReviewNPJ digital medicine2020
A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.
Review in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 4 of them syntheses that pooled 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.
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
119 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- What are the key predictors of visual function recovery in uveitis? A systematic review and meta-analysis unraveling structural, genetic, and immunological determinants.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Pooled it
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.RMD open · 2023Pooled it
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Advances and pitfalls in anti-double-stranded DNA autoantibody detection and interpretation.World journal of biological chemistry · 2026Review
- The evolving landscape of inflammation-related clinical trials: a 25-year registry-based network analysis from a clinical pharmacology perspective.Translational and clinical pharmacology · 2026Article
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Radiograph-Based Deep Learning Model to Support Finger Joint Selection for Ultrasound Examination in Rheumatoid Arthritis.Diagnostics (Basel, Switzerland) · 2026Article
- A Focused Comparative Review of Innovative Therapeutics Across Autoimmune and Chronic Inflammatory Diseases.Life (Basel, Switzerland) · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Characterizing patients who benefit from mature medical AI models in real-world clinical applications.PLOS digital health · 2026Article
- AI-driven computational methods and benchmarking for T-cell antigen identification.Briefings in bioinformatics · 2026Review
- Review
- Impact of COVID-19 on inflammatory disease research: a literature analysis of 77,575 PubMed articles.Reumatologia · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for depression risk in sleep disorder patients.BMC psychiatry · 2025Article
- Machine learning classification of inflammatory bowel disease activity using white blood cell subsets.BMJ open gastroenterology · 2025Article
- Association of the dietary index for gut microbiota with metabolic syndrome and its components combining interpretable machine learning algorithms.Journal of health, population, and nutrition · 2025Article
- Diagnostic Value of Machine Learning Models in Inflammation of Unknown Origin.Journal of clinical medicine · 2025Article
- Article
- Artificial intelligence in inflammatory bowel disease.Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association · 2025Review
59 more citing papers are in PubMed but not listed here.
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 diseases are chronic, multifactorial conditions. Through machine learning (ML), a branch of the wider field of artificial intelligence, it is possible to extract patterns within patient data, and exploit these patterns to predict patient outcomes for improved clinical management. Here, we surveyed the use of ML methods to address clinical problems in autoimmune disease. A systematic review was conducted using MEDLINE, embase and computers and applied sciences complete databases. Relevant papers included "machine learning" or "artificial intelligence" and the autoimmune diseases search term(s) in their title, abstract or key words. Exclusion criteria: studies not written in English, no real human patient data included, publication prior to 2001, studies that were not peer reviewed, non-autoimmune disease comorbidity research and review papers. 169 (of 702) studies met the criteria for inclusion. Support vector machines and random forests were the most popular ML methods used. ML models using data on multiple sclerosis, rheumatoid arthritis and inflammatory bowel disease were most common. A small proportion of studies (7.7% or 13/169) combined different data types in the modelling process. Cross-validation, combined with a separate testing set for more robust model evaluation occurred in 8.3% of papers (14/169). The field may benefit from adopting a best practice of validation, cross-validation and independent testing of ML models. Many models achieved good predictive results in simple scenarios (e.g. classification of cases and controls). Progression to more complex predictive models may be achievable in future through integration of multiple data types.
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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.