CAPABILITIES OF ARTIFICIAL INTELLIGENCE IN MODERN RHEUMATOLOGY. LITERATURE REVIEW
Relevance: The application of artificial intelligence (AI) in clinical practice opens new opportunities to improve diagnostic and prognostic accuracy in rheumatology, a field characterized by high disease heterogeneity and the complexity of interpreting visual and clinical data
Search strategy: An analysis was conducted on 58 publications selected from 467 relevant sources (2015–2025), identified through PubMed, Google Scholar, and CyberLeninka databases. Included were original studies and review articles focused on the use of machine learning and deep learning methods in rheumatology.
Results: AI is increasingly being implemented to address tasks related to early diagnosis, disease course prediction, and personalized therapy selection in rheumatoid arthritis, spondyloarthritis, systemic lupus erythematosus, and osteoarthritis. Convolutional neural networks, multi-omics approaches, and adaptive prediction models were found to be the most effective, demonstrating high accuracy (with AUC values up to 0.85). Despite this potential, clinical integration remains limited due to small training datasets, the need for external validation, and insufficient model standardization.
Conclusions: AI holds significant practical value in rheumatology. Its integration into healthcare systems requires regulatory frameworks, medical staff training, the development of digital infrastructure, and interdisciplinary collaboration.
Petrova Yuliya Viktorovna – Master of Medicine,Department of Internal Medicine and Rheumatology NCJSC «Semey Medical University», 103 AbayStreet, Semey, 071400, Kazakhstan; Email: yuliya.petrova@smu.edu.kz; phone number: +7(771) 537 6060; https://orcid.org/0000-0003-1910-7169
Goremykina Maiya Valentinovna - Candidate of Medical Sciences, Associate Professor, Department of Internal Medicine and Rheumatology, NCJSC «Semey Medical University», 103 AbayStreet, Semey, 071400, Kazakhstan;Email: maya.goremykina@smu.edu.kz;phone number: +7 (777) 390 8234; https://orcid.org/0000-0002-5433-7771
Ivanova Raifa Latyfovna– Professor, Doctor of Medical Sciences, Department of Internal Medicine and Rheumatology NCJSC «Semey Medical University», 103 AbayStreet, Semey, 071400, Kazakhstan; Email: raifa.ivanova@smu.edu.kz; phone number: +7(777)147 2892; https://orcid.org/0000-0001-9851-2255
Kozhakhmetova Dana Kenzhebaevna – PhD, assistant of the Department of Internal Diseases and Rheumatology, NСJSC «Semey Medical University», Address: 071400, Republic of Kazakhstan, Abai Region, Semey, 103 AbayKunanbayev St., Email: dana_ken@mail.ru; phone number: +7(702) 705 1403; https://orcid.org/0000-0002-8367-1461
Botabayeva Ainur - MD, assistant of the Department of Internal Diseases and Rheumatology, NСJSC «Semey Medical University»,Address: 071400, Republic of Kazakhstan, Abai Region, Semey, 103 AbayKunanbayev St., e-mail: aibota7878@mail.ru Phone: +7 (701) 100 2098 https://orcid.org/0009-0008-9228-7788
Razvodova Alexandra – MD, Rheumatologist, LLP «Hippocrates», Address: 110000, Republic of Kazakhstan, Kostanay, 151, 1st May Street, Building 7; phone number:+7(705) 727 0571 https://orcid.org/0000-0002-8367-1461
1. Байтурганов Т.М., Айткожин Г.К., Жунусова Л.Э., Мұса Ә.М., Тілеуов Ә.Б. Использование искусственного интеллекта пациент-центрированной онлайн-системы Saubol в превентивной медицине Казахстана: обзор литературы. Вестник науки и творчества. 2023. № 8 (90). C. 20-27
2. Adamichou C., Genitsaridi I., Nikolopoulos D. et al. Lupus or not? SLE Risk Probability Index (SLERPI): a simple, clinician-friendly machine learning-based model to assist the diagnosis of systemic lupus erythematosus. Annals of the Rheumatic Diseases. 2021. Vol. 80. P. 758–766.
3. Baek I.W., Jung S.M., Park Y.J. et al. Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting. Arthritis Research & Therapy. 2023. Vol. 25. P. 65.
4. Bird A., Oakden-Rayner L., McMaster C. et al. Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint // Arthritis Res Ther. 2022. Vol. 24. P. 268.
5. Bouget V., Duquesne J., Cournède P.H. et al. Machine learning predicts response to methotrexate in rheumatoid arthritis: results on the ESPOIR, T-REACH and Leiden cohorts. Ann Rheum Dis. 2022. Vol. 81. P. 535.
6. Bouget V., Duquesne J., Hassler S. et al. Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts. RMD Open. 2022. 8:e002442 P.1-9
7. Bressem K.K., Vahldiek J.L., Adams L. et al. Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance. Arthritis Research & Therapy. 2021. Vol. 23. P. 106.
8. Bressem K.K., Adams L.C., Proft F. et al. Deep learning detects changes indicative of axial spondyloarthritis at MRI of sacroiliac joints.Radiology. 2022. Vol. 305. P. 655–665.
9. Casaburi G., O’Malley T., Holscher T. et al. Targeted synovial tissue RNA-Seq coupled with artificial intelligence accurately predicts early rheumatoid arthritis patients likely to respond to CsDMARDs. Arthritis Rheumatol. 2022. Vol. 74. P. 1846.
10. Ceccarelli F., Lapucci M., Olivieri G. et al. Can machine learning models support physicians in systemic lupus erythematosus diagnosis? Results from a monocentric cohort. Joint Bone Spine. 2022. Vol. 89. Article 105292.
11. Ceccarelli F., Natalucci F., Picciariello L. et al. Application of machine learning models in systemic lupus erythematosus. Int. J. Mol. Sci. 2023. Vol. 24, № 5. Article 4514.
12. Cohen S., Wells A. F., Curtis J. R. et al. A molecular signature response classifier to predict inadequate response to tumor necrosis factor-α inhibitors: the NETWORK-004 prospective observational study.RheumatolTher. 2021. Vol. 8, № 3. P. 1159–1176.
13. Deimel T., Aletaha D., Langs G. OP0059 Autoscora: deep learning to automate scoring of radiographic progression in rheumatoid arthritis. Ann Rheum Dis. 2020. – Vol. 79 (Suppl. 1). P. 39–40.
14. Demanse D., Saxer F., Lustenberger P. et al. Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database.Semin Arthritis Rheum. 2023. Vol. 58. Article 152140.
15. Esteva A. et al. A guide to deep learning in healthcare. Nat Med. 2019. Vol. 25. P. 24–29.
16. Folle L., Simon D., Tascilar K. et al. Deep learning-based classification of inflammatory arthritis by identification of joint shape patterns: how neural networks can tell us where to “deep dive” clinically. Front. Med. 2022. Vol. 9. Article 850552.
17. Folle L., Bayat S., Kleyer A. et al. Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis. Rheumatology (Oxford). 2022. Vol. 61, № 12. P. 4945–4951.
18. Hao X., Zheng D., Khan M. et al. Machine learning models for predicting adverse pregnancy outcomes in pregnant women with systemic lupus erythematosus. Diagnostics. 2023. Vol. 13, № 4. Article 612.
19. Huang K., Wu X., Li Y. et al. Artificial intelligence–based psoriasis severity assessment: real-world study and application. J. Med. Internet Res. 2023. Vol. 25. Article e44932.
20. Huang Y.J. et al. Automatic joint space assessment in hand radiographs with deep learning among patients with rheumatoid arthritis. Arthritis Rheumatol. 2020. P. 72 (Suppl. 10).
21. Iglesias L.L. et al. A primer on deep learning and convolutional neural networks for clinicians. Insights Imaging. 2021. Vol. 12. P. 117.
22. Jans L., Chen M., Elewaut D. et al. MRI-based Synthetic CT in the Detection of Structural Lesions in Patients with Suspected Sacroiliitis: Comparison with MRI. Radiology. 2021. Vol. 298, № 2. P. 343–349.
23. Jorge A.M., Smith D., Wu Z. et al. Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations. Lupus. 2022. Vol. 31, №11. P. 1296–1305.
24. Kalweit M., Walker U.A., Finckh A. et al. Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network.PLoS ONE. 2021. Vol. 16, № 6. Article e0252289.
25. Kepp F.H., Huber F.A., Wurnig M.C. et al. Differentiation of inflammatory from degenerative changes in the sacroiliac joints by machine learning supported texture analysis.Eur J Radiol. 2021. Vol. 140. Article 109755.
26. Kohane I.S. et al. What every reader should know about studies using electronic health record data but may be afraid to ask. J Med Internet Res. 2021. Vol. 23. Article e22219.
27. Labinsky H., Ukalovic D., Hartmann F. et al. An AI-powered clinical decision support system to predict flares in rheumatoid arthritis: a pilot study. Diagnostics. 2023. Vol. 13, № 1. P. 148.
28. LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015. Vol. 521. P. 436–444.
29. Lee S., Kang S., Eun Y. et al. Machine learning-based prediction model for responses of bDMARDs in patients with rheumatoid arthritis and ankylosing spondylitis. Arthritis Res Ther. 2021. Vol. 23. P. 254.
30. Lee S., Eun Y., Kim H. et al. Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis.Sci Rep. 2020. Vol. 10. Article 20299.
31. Liu Y. et al. How to read articles that use machine learning: users’ guides to the medical literature. JAMA. 2019. Vol. 322. P. 1806–1816.
32. Mielnik P., Fojcik M., Segen J., Kulbacki M.A novel method of synovitis stratification in ultrasound using machine learning algorithms: results from clinical validation of the MEDUSA Project. Ultrasound Med Biol. 2018. Vol. 44. P. 489–494.
33. Mongan J., Moy L., Kahn C.E. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers.RadiolArtifIntell. 2020. Vol. 2. Article 200029.
34. Mukhammedzhanova D.M., Suleimenova M.AI-based remote patient monitoring systems in Kazakhstan.Žaršysy. 2024. Vol. 35, № 4. P.87-99
35. Murakami S., Hatano K., Tan J. et al. Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network.Multimed Tools Appl. 2018. Vol. 77, № 9. P. 10921–10937.
36. Nelson A.E., Keefe T.H., Schwartz T.A. et al.Biclustering reveals potential knee OA phenotypes in exploratory analyses: data from the osteoarthritis initiative.PLoS ONE. 2022. Vol. 17, № 5. Article e0266964.
37. Nendel N., Eiben R., Frommhold J. et al. Characterization of preclinical rheumatoid arthritis and psoriatic arthritis by clinical and imaging parameters and their potential role in the prediction of arthritis. Ann Rheum Dis. 2024. Vol. 83. P. 605.
38. Norgeot B., Glicksberg B.S., Trupin L. et al. Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis. NPJ Digit Med. 2019. Vol. 2, № 3. P.1-12
39. Peng J., Jury E.C., Dönnes P., Ciurtin C. Machine learning techniques for personalised medicine approaches in immune-mediated chronic inflammatory diseases: applications and challenges. Front Pharmacol. 2021. Vol. 12. Article 720694.
40. Phatak S., Chakraborty S., Goel P.K. Computer vision detects inflammatory arthritis in standardized smartphone photographs in an Indian patient cohort. Front Med. 2023. Vol. 10. P.1-7
41. Rekha G., Afsana Sk., Anjum M.R., Padma K.R. Mohan M. Artificial intelligence in rheumatology. 2024. P. 1–18.
42. Roels J., De Craemer A.S., Renson T. et al. Machine learning pipeline for predicting bone marrow edema along the sacroiliac joints on magnetic resonance imaging. Arthritis Rheumatol. 2023. P. 2169-2177
43. Rohrbach J., Reinhard T., Sick B., Dürr O. Bone erosion scoring for rheumatoid arthritis with deep convolutional neural networks.ComputElectr Eng. 2019. Vol. 78. P. 472–481.
44. Rudge A., McHugh N., Tillett W., Smith T. An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records. Ann Rheum Dis. 2025. P.575-583
45. Scott I., Carter S., Coiera E. Clinician checklist for assessing suitability of machine learning applications in healthcare. BMJ Health Care Inf. 2021. Vol. 28. Article e100251.
46. Sonomoto K., Fujino Y., Tanaka H. et al. A machine learning approach for prediction of CDAI remission with TNF inhibitors: a concept of precision medicine from the FIRST registry.RheumatolTher. 2024. Vol. 11, № 3. P. 709–736.
47. Steinberg J., Southam L., Fontalis A. et al. Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis. Ann Rheum Dis. 2021. Vol. 80, № 8. P. 1070–1074.
48. Stoel B.C. et al. Deep learning in rheumatoid arthritis imaging: hype or hope? Semin Arthritis Rheum. 2019. Vol. 49 (Suppl. 1). P. S25–S28.
49. Tao W., Concepcion A.N., Vianen M. et al.Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis. Arthritis Rheumatol. 2021. Vol. 73, № 2. P. 212–222.
50. Trajerova M., Kriegova E., Mikulkova Z. et al. Knee osteoarthritis phenotypes based on synovial fluid immune cells correlate with clinical outcome trajectories. Osteoarthritis Cartilage. 2022. Vol. 30, № 12. P. 1583–1592.
51. Ukalovic D., Leeb B.F., Rintelen B. et al. Prediction of ineffectiveness of biological drugs using machine learning and explainable AI methods: data from the Austrian Biological Registry BioReg. Arthritis Res Ther. 2024. P.26:44
52. Usategui I., Arroyo Y., Torres A.M. et al. Systemic lupus erythematosus: how machine learning can help distinguish between infections and flares. Bioengineering. 2024. Vol. 11, № 1. P. 90.
53. Venerito V., Bilgin E., Iannone F., Kiraz S. AI am a rheumatologist: a practical primer to large language models for rheumatologists. Rheumatology (Oxford). 2023. Vol. 62. P. 3256–3260.
54. Venerito V., Puttaswamy D., Iannone F., Gupta L. Large language models and rheumatology: a comparative evaluation. The Lancet Rheumatology. 2023. Vol. 5. P. e574–e578.
55. Vodencarevic A., Tascilar K., Hartmann F., et al. Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs. Arthritis Res Ther. 2021. Vol. 23. P. 67.
56. Wang M., Li R., Qi H. et al. Metabolomics and machine learning identify metabolic differences and potential biomarkers for frequent versus infrequent gout flares. Arthritis Rheumatol. 2023. Vol. 75, № 12. P. 2252–2264.
57. Wang R., Dasgupta A., Ward M.M. Predicting probability of response to tumor necrosis factor inhibitors for individual patients with ankylosing spondylitis. JAMA Netw Open. 2022. Vol. 5, № 3. Article e222312.
58. Yu K.H., Beam A.L., Kohane I.S. Artificial intelligence in healthcare. Nat Biomed Eng. 2018. Vol. 2. P. 719–731.
Количество просмотров: 45
Категория статей:
Обзор литературы
Библиографическая ссылка
Petrova Y.V., Goremykina M.V., Ivanova R.L., Kozhakhmetova D.K., Botabayeva A.S., Razvodova A.A. Capabilities of Artificial Intelligence in Modern Rheumatology. Literature review // Nauka I Zdravookhranenie [Science&Healthcare]. 2025. Vol.27(3), pp.236-244. doi 10.34689/SH.2025.27.3.025Похожие публикации:
RADIATION EXPOSURE IN THE SEMIPALATINSK REGION: TRANSGENERATIONAL RISKS AND DOSIMETRY APPROACHES
TRENDS AND PATTERNS OF MALE REPRODUCTIVE SYSTEM DISEASES IN THE CONTEXT OF MEDICAL AND DEMOGRAPHIC INDICATORS. LITERATURE REVIEW
INFECTIOUS COMPLICATIONS OF UROLOGICAL INTERVENTIONS FOR UROLITHIASIS. LITERATURE REVIEW
PREVALENCE OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE AS A GLOBAL HEALTH PROBLEM
THE QUALITY OF LIFE OF PATIENTS WITH COPD. LITERATURE REVIEW