The incorporation of artificial intelligence (AI) into medical practice has considerably improved the treatment of autoimmune disorders, opening new avenues for personalized therapy. This study examines advances in AI-driven therapeutic options for autoimmune illnesses, including both current and developing treatments. Traditional therapies for autoimmune illnesses, such as immunosuppressive therapy and biologics, attempt to alleviate symptoms but frequently fall short of offering personalized care. Emerging approaches, such as precision medicine and artificial intelligence, are altering the landscape by harnessing massive volumes of patient data to better customize therapies. AI holds the ability to transform autoimmune disease therapy by enhancing diagnosis, discovering biomarkers, optimizing drug development, and personalized treatment procedures. Real-world applications and case studies are examined to demonstrate how machine learning algorithms have improved treatment tactics for rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis. While AI has many advantages, like enhanced diagnosis accuracy and personalized therapy, it also has drawbacks, such as data privacy, the requirement for vast datasets, algorithmic bias, and a lack of explain ability. This study emphasizes the advantages of AI, such as improved patient stratification and predictive modelling, while also discussing its drawbacks, such as ethical problems and the possibility of data exploitation. AI presents intriguing prospects for treating autoimmune diseases, but more research and cooperation are required to overcome current difficulties and completely integrate AI into clinical practice.

1. Mackay IR. Autoimmune Diseases. Encyclopedia of Immunology. Published online 1998: 287–292. doi: 10.1006/rwei.1999.0074

2. Rose NR, Bona C. Defining criteria for autoimmune diseases (Witebsky’s postulates revisited). Immunology today. 1993; 14(9): 426–430.

3. Davidson A, Diamond B. Autoimmune Diseases. Mackay IR, Rosen FS, eds. New England Journal of Medicine. 2001; 345(5): 340–350. doi: 10.1056/nejm200108023450506

4. Ngo ST, Steyn FJ, McCombe PA. Gender differences in autoimmune disease. Frontiers in Neuroendocrinology. 2014; 35(3): 347–369. doi: 10.1016/j.yfrne.2014.04.004

5. Rosenblum MD, Remedios KA, Abbas AK. Mechanisms of human autoimmunity. Journal of Clinical Investigation. 2015; 125(6): 2228–2233. doi: 10.1172/jci78088

6. Ghobadinezhad F, Ebrahimi N, Mozaffari F, et al. The emerging role of regulatory cell-based therapy in autoimmune disease. Frontiers in Immunology. 2022; 13. doi: 10.3389/fimmu.2022.1075813

7. Pisetsky DS. Pathogenesis of autoimmune disease. Nature Reviews Nephrology. 2023; 19(8): 509–524. doi: 10.1038/s41581-023-00720-1

8. Vivas AJ, Boumediene S, Tobón GJ. Predicting autoimmune diseases: A comprehensive review of classic biomarkers and advances in artificial intelligence. Autoimmunity Reviews. 2024; 23(9): 103611. doi: 10.1016/j.autrev.2024.103611

9. Ahmed Z. Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities. Emerging Topics in Life Sciences. 2022; 6(2): 215–225. doi: 10.1042/etls20210244

10. Sahu A, Mishra J, Kushwaha N. Artificial Intelligence (AI) in Drugs and Pharmaceuticals. Combinatorial Chemistry & High Throughput Screening. 2022; 25(11): 1818–1837. doi:10.2174/1386207325666211207153943

11. Cortes A, Brown MA. Promise and pitfalls of the Immunochip. Arthritis Research & Therapy. 2010; 13(1): 101. doi: 10.1186/ar3204

12. Burmester GR, Pope JE. Novel treatment strategies in rheumatoid arthritis. The Lancet. 2017; 389(10086): 2338–48.

13. Dias SS, Isenberg DA. Advances in systemic lupus erythematosus. Medicine. 2014; 42(3): 126–133. doi: 10.1016/j.mpmed.2013.12.013

14. Jacobson DL, Gange SJ, Rose NR, et al. Epidemiology and Estimated Population Burden of Selected Autoimmune Diseases in the

15. Noseworthy JH, Lucchinetti C, Rodriguez M, et al. Multiple Sclerosis. New England Journal of Medicine. 2000; 343(13): 938–952. doi: 10.1056/nejm200009283431307

16. Cooper GS, Stroehla BC. The epidemiology of autoimmune diseases. Autoimmunity reviews. 2003;2(3): 119–125.

17. Petersen JS, Dyrberg T, Karlsen AE, Molvig J. Type 1 (insulin-dependent) diabetes mellitus: an overview of autoimmune disease and the role of interleukin-1. Diabetologia. 1997; 40(Suppl 2): S26–S34.

18. Lerner A, Jeremias P, Matthias T. The World Incidence and Prevalence of Autoimmune Diseases is Increasing. International Journal of Celiac Disease. 2015; 3(4): 151–155. doi: 10.12691/ijcd-3-4-8

19. Baldini E, Odorisio T, Tuccilli C, et al. Thyroid diseases and skin autoimmunity. Reviews in Endocrine and Metabolic Disorders. 2018; 19(4): 311–323. doi: 10.1007/s11154-018-9450-7

20. Singh M, Wambua S, Lee SI, et al. Autoimmune diseases and adverse pregnancy outcomes: an umbrella review. BMC Medicine. 2024; 22(1): 94. doi: 10.1186/s12916-024-03309-y

21. Kronzer VL, Kimbrough BA, Crowson CS, et al. Incidence, Prevalence, and Mortality of Dermatomyositis: A Population‐Based Cohort Study. Arthritis Care & Research. 2022; 75(2): 348–355. doi: 10.1002/acr.24786

22. Chen J, Tian DC, Zhang C, et al. Incidence, mortality, and economic burden of myasthenia gravis in China: A nationwide population-based study. The Lancet Regional Health—Western Pacific. 2020; 5: 100063. doi: 10.1016/j.lanwpc.2020.100063

23. Caturegli P, De Remigis A, Rose NR. Hashimoto thyroiditis: Clinical and diagnostic criteria. Autoimmunity Reviews. 2014; 13(4–5): 391–397. doi: 10.1016/j.autrev.2014.01.007

24. Mahil SK, Wilson N, Dand N, et al. Psoriasis treat to target: defining outcomes in psoriasis using data from a real-world, population—based cohort study (the British Association of Dermatologists Biologics and Immunomodulators Register, BADBIR ). British Journal of Dermatology. 2020; 182(5): 1158–1166. doi: 10.1111/bjd.18333

25. Bartalena L. Diagnosis and management of Graves disease: a global overview. Nature Reviews Endocrinology. 2013; 9(12): 724–734. doi: 10.1038/nrendo.2013.193

26. Bai JC, Ciacci C. World Gastroenterology Organisation Global Guidelines. Journal of Clinical Gastroenterology. 2017; 51(9): 755–768. doi: 10.1097/mcg.0000000000000919

27. Bugălă NM, Carsote M, Stoica LE, et al. New Approach to Addison Disease: Oral Manifestations Due to Endocrine Dysfunction and Comorbidity Burden. Diagnostics. 2022; 12(9): 2080. doi: 10.3390/diagnostics12092080

28. Voulgaris TA, Karamanolis GP. Esophageal manifestation in patients with scleroderma. World Journal of Clinical Cases. 2021; 9(20): 5408–5419. doi: 10.12998/wjcc.v9.i20.5408

29. Stavropoulos PG, Soura E, Kanelleas A, et al. Reactive Arthritis. Journal of the European Academy of Dermatology and Venereology. 2015; 29(3): 415–424. doi: 10.1111/jdv.12741

30. Benagiano M, Bianchi P, D’Elios MM, et al. Autoimmune diseases: Role of steroid hormones. Best Practice & Research Clinical Obstetrics & Gynaecology. 2019; 60: 24–34. doi: 10.1016/j.bpobgyn.2019.03.001

31. Williams DM. Clinical Pharmacology of Corticosteroids. Respiratory Care. 2018; 63(6): 655–670. doi: 10.4187/respcare.06314

32. Reichardt SD, Amouret A, Muzzi C, et al. The Role of Glucocorticoids in Inflammatory Diseases. Cells. 2021; 10(11): 2921. doi: 10.3390/cells10112921

33. Gisbert JP, Chaparro M. Systematic review with meta-analysis: inflammatory bowel disease in the elderly. Alimentary Pharmacology & Therapeutics. 2014; 39(5): 459–477. doi: 10.1111/apt.12616

34. Christofi T, Baritaki S, Falzone L, et al. Current Perspectives in Cancer Immunotherapy. Cancers. 2019; 11(10): 1472. doi: 10.3390/cancers11101472

35. Feldmann M, Maini RN. Anti-TNFα Therapy of Rheumatoid Arthritis: What Have We Learned? Annual Review of Immunology. 2001; 19(1): 163–196. doi: 10.1146/annurev.immunol.19.1.163

36. Li SJ, Perez-Chada LM, Merola JF. TNF Inhibitor-Induced Psoriasis: Proposed Algorithm for Treatment and Management. Journal of Psoriasis and Psoriatic Arthritis. 2018; 4(2): 70–80. doi: 10.1177/2475530318810851

37. Thoma LM, Li LC, White DK, et al. Physical Therapists Play a Key Role in the Comprehensive Management of Rheumatoid Arthritis. Arthritis Care & Research. 2023; 75(8): 1625–1628. doi: 10.1002/acr.25123

38. Göksel Karatepe A, Günaydin R, Türkmen G, et al. Effects of home-based exercise program on the functional status and the quality of life in patients with rheumatoid arthritis: 1-year follow-up study. Rheumatology International. 2009; 31(2): 171–176. doi: 10.1007/s00296-009-1242-7

39. Vojdani A, O’Bryan T, Green JA, et al. Immune Response to Dietary Proteins, Gliadin and Cerebellar Peptides in Children with Autism. Nutritional Neuroscience. 2004; 7(3): 151–161. doi: 10.1080/10284150400004155

40. Skoldstam L, Hagfors L, Johansson G.. An experimental study of a Mediterranean diet intervention for patients with rheumatoid arthritis. Annals of the Rheumatic Diseases. 2003; 62(3): 208–214. doi: 10.1136/ard.62.3.208

41. Sharif K, Watad A, Bragazzi NL, et al. Physical activity and autoimmune diseases: Get moving and manage the disease. Autoimmunity Reviews. 2018; 17(1): 53–72. doi: 10.1016/j.autrev.2017.11.010

42. Schäfer C, Keyßer G. Lifestyle Factors and Their Influence on Rheumatoid Arthritis: A Narrative Review. Journal of Clinical Medicine. 2022; 11(23): 7179. doi: 10.3390/jcm11237179

43. Dammes N, Peer D. Monoclonal antibody-based molecular imaging strategies and theranostic opportunities. Theranostics. 2020; 10(2): 938–955. doi: 10.7150/thno.37443

44. Keating GM. Panitumumab: a review of its use in metastatic colorectal cancer. Drugs. 2010; 70(8): 1059–1078. doi: 10.2165/11205090-000000000-00000

45. McElvaney OJ, Curley GF, Rose-John S, McElvaney NG. Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med. 2021; 9(6): 643–654. doi: 10.1016/S2213-2600(21)00103-X

46. O’Shea JJ, Kontzias A, Yamaoka K, et al. Janus kinase inhibitors in autoimmune diseases. Annals of the Rheumatic Diseases. 2013; 72(suppl 2): ii111–ii115. doi: 10.1136/annrheumdis-2012-202576

47. Nash RA, Hutton GJ, Racke MK, et al. High-Dose Immunosuppressive Therapy and Autologous Hematopoietic Cell Transplantation for Relapsing-Remitting Multiple Sclerosis (HALT-MS). JAMA Neurology. 2015; 72(2): 159. doi: 10.1001/jamaneurol.2014.3780

48. Le Blanc K, Mougiakakos D. Multipotent mesenchymal stromal cells and the innate immune system. Nature Reviews Immunology. 2012; 12(5): 383–396. doi: 10.1038/nri3209

49. Burt RK, Shah SJ, Dill K, et al. Autologous non-myeloablative haemopoietic stem-cell transplantation compared with pulse cyclophosphamide once per month for systemic sclerosis (ASSIST): an open-label, randomised phase 2 trial. Lancet. 2011; 378(9790): 498–506. doi: 10.1016/S0140-6736(11)60982-3

50. Burt RK, Oliveira MC, Shah SJ, et al. Cardiac involvement and treatment-related mortality after non-myeloablative haemopoietic stem-cell transplantation with unselected autologous peripheral blood for patients with systemic sclerosis: a retrospective analysis. Lancet. 2013; 381(9872): 1116–1124. doi: 10.1016/S0140-6736(12)62114-X

51. Choi EW, Lee HW, Shin IS, et al. Comparative Efficacies of Long-Term Serial Transplantation of Syngeneic, Allogeneic, Xenogeneic, or CTLA4Ig-Overproducing Xenogeneic Adipose Tissue-Derived Mesenchymal Stem Cells on Murine Systemic Lupus Erythematosus. Cell Transplantation. 2016; 25(6): 1193–1206. doi: 10.3727/096368915x689442

52. Doudna JA, Charpentier E. The new frontier of genome engineering with CRISPR-Cas9. Science. 2014; 346(6213). doi: 10.1126/science.1258096

53. Sadelain M, Brentjens R, Rivière I. The promise and potential pitfalls of chimeric antigen receptors. Current Opinion in Immunology. 2009; 21(2): 215–223. doi: 10.1016/j.coi.2009.02.009

54. Singh DD, Hawkins RD, Lahesmaa R, et al. CRISPR/Cas9 guided genome and epigenome engineering and its therapeutic applications in immune mediated diseases. Seminars in Cell & Developmental Biology. 2019; 96: 32–43. doi: 10.1016/j.semcdb.2019.05.007

55. Van Zeebroeck L, Arroyo Hornero R, Côrte-Real BF, et al. Fast and Efficient Genome Editing of Human FOXP3+ Regulatory T Cells. Frontiers in Immunology. 2021; 12. doi: 10.3389/fimmu.2021.655122

56. Dai X, Blancafort P, Wang P, et al. Innovative Precision Gene‐Editing Tools in Personalized Cancer Medicine. Advanced Science. 2020; 7(12). doi: 10.1002/advs.201902552

57. Bhattacharjee G, Gohil N, Khambhati K, et al. Current approaches in CRISPR-Cas9 mediated gene editing for biomedical and therapeutic applications. Journal of Controlled Release. 2022; 343: 703–723. doi: 10.1016/j.jconrel.2022.02.005

58. Guttinger S. Trust in Science: CRISPR–Cas9 and the Ban on Human Germline Editing. Science and Engineering Ethics. 2018; 24(4): 1077–1096. doi: 10.1007/s11948-017-9931-1

59. Gyngell C, Douglas T, Savulescu J. The Ethics of Germline Gene Editing. Journal of Applied Philosophy. 2017; 34(4): 498–513. doi: 10.1111/japp.12249

60. Firestein GS, McInnes IB. Immunopathogenesis of Rheumatoid Arthritis. Immunity. 2017; 46(2): 183–196. doi: 10.1016/j.immuni.2017.02.006

61. Smolen JS, Landewé RBM, Bijlsma JWJ, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2019 update. Annals of the Rheumatic Diseases. 2020; 79(6): 685–699. doi: 10.1136/annrheumdis-2019-216655

62. Schäcke H, Döcke WD, Asadullah K. Mechanisms involved in the side effects of glucocorticoids. Pharmacol Ther. 2002; 96(1): 23–43. doi: 10.1016/s0163-7258(02)00297-8

63. Ramos-Casals M, Roberto-Perez-Alvarez, Diaz-Lagares C, et al. Autoimmune diseases induced by biological agents. Autoimmunity Reviews. 2010; 9(3): 188–193. doi: 10.1016/j.autrev.2009.10.003

64. Robin DiMatteo M, Giordani PJ, Lepper HS, et al. Patient Adherence and Medical Treatment Outcomes. Medical Care. 2002; 40(9): 794–811. doi: 10.1097/00005650-200209000-00009

65. Marengo MF, Suarez-Almazor ME. Improving treatment adherence in patients with rheumatoid arthritis: what are the options? International Journal of Clinical Rheumatology. 2015; 10(5): 345–356. doi: 10.2217/ijr.15.39

66. Moingeon P. Artificial intelligence-driven drug development against autoimmune diseases. Trends in Pharmacological Sciences. 2023; 44(7): 411–424. doi: 10.1016/j.tips.2023.04.005

67. Gerussi A, Scaravaglio M, Cristoferi L, et al. Artificial intelligence for precision medicine in autoimmune liver disease. Frontiers in Immunology. 2022; 13. doi: 10.3389/fimmu.2022.966329

68. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 2019; 29(2): 102–127. doi: 10.1016/j.zemedi.2018.11.002

69. Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics. 2017; 19(6): 1236–1246. doi: 10.1093/bib/bbx044

70. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019; 25(1): 44–56. doi: 10.1038/s41591-018-0300-7

71. MacEachern SJ, Forkert ND. Machine learning for precision medicine. Genome. 2021; 64(4): 416–425. doi: 10.1139/gen-2020-0131

72. Zhavoronkov A. Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry. Molecular Pharmaceutics. 2018; 15(10): 4311–4313. doi: 10.1021/acs.molpharmaceut.8b00930

73. Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nature Materials. 2019; 18(5): 435–441. doi: 10.1038/s41563-019-0338-z

74. Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discovery Today. 2019; 24(3): 773–780. doi: 10.1016/j.drudis.2018.11.014

75. Davergne T, Rakotozafiarison A, Servy H, et al. Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020? Sensors. 2020; 20(17): 4797. doi: 10.3390/s20174797

76. Kamei T, Kanamori T, Yamamoto Y, et al. The use of wearable devices in chronic disease management to enhance adherence and improve telehealth outcomes: A systematic review and meta-analysis. Journal of Telemedicine and Telecare. 2020; 28(5): 342–359. doi: 10.1177/1357633x20937573

77. Keesara S, Jonas A, Schulman K. Covid-19 and Health Care’s Digital Revolution. New England Journal of Medicine. 2020; 382(23). doi: 10.1056/nejmp2005835

78. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education. 2023; 23(1): 689. doi: 10.1186/s12909-023-04698-z

79. Yagin F, Alkhateeb A, Raza A, et al. An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites. Diagnostics. 2023; 13(23): 3495. doi: 10.3390/diagnostics13233495

80. Vodencarevic A, Tascilar K, et al. Advanced machine learning for predicting individual risk of flares in rheumatoid arthritis patients tapering biologic drugs. Arthritis Research & Therapy. 2021; 23(1). doi: 10.1186/s13075-021-02439-5

81. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639): 115–118. doi: 10.1038/nature21056

82. Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatology and Therapy. 2022; 9(5): 1249–1304. doi: 10.1007/s40744-022-00475-4

83. Mukherjee S, Suleman S, Pilloton R, et al. State of the Art in Smart Portable, Wearable, Ingestible and Implantable Devices for Health Status Monitoring and Disease Management. Sensors. 2022; 22(11): 4228. doi: 10.3390/s22114228

84. Wang S, Hou Y, Li X, et al. Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis. Frontiers in Pharmacology. 2021; 12. doi: 10.3389/fphar.2021.765435

85. Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev. 2024 Feb;23(2):103496. doi: 10.1016/j.autrev.2023.103496.

86. Zhang P, Fonnesbeck C, Schmidt DC, et al. Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study. JMIR mHealth and uHealth. 2022; 10(3): e21959. doi: 10.2196/21959

87. Kahkoska AR, Shah KS, Kosorok MR, et al. Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study. Journal of Diabetes Science and Technology. 2024; 18(5): 1079–1086. doi: 10.1177/19322968221149040

88. Pillai AS. Utilizing Deep Learning in Medical Image Analysis for Enhanced Diagnostic Accuracy and Patient Care: Challenges, Opportunities, and Ethical Implications. Journal of Deep Learning in Genomic Data Analysis. 2021; 1(1): 1–7.

89. Johnson KB, Wei W, Weeraratne D, et al. Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science. 2021; 14(1): 86–93. doi: 10.1111/cts.12884

90. Yadav S, Singh A, Singhal R, et al. Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy. 2024; 2(3): 367–380. doi: 10.1016/j.ipha.2024.02.009

91. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education. 2023; 23(1): 689. doi: 10.1186/s12909-023-04698-z