Artificial Intelligence (AI)  has arisen as a genuine tool in diagnosing and managing  of various medical problems including autoimmune diseases. This comprehensive review focused on the possible applications, approaches, and future leaderships of AI skills in the diagnosis of autoimmune diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), ankylosing spondylitis (AS), psoriatic arthritis (PsA), systemic sclerosis (SSc), Sjögren’s syndrome (SjS), and Behcet’s disease. The review shows that AI-based algorithms, including artificial neural networks (ANNs), support vector machines (SVMs), convolutional neural networks (CNNs), and random forests, have attained noteworthy results and statistics . For example, RA diagnosis achieved an  excellent accuracy by incorporating clinical, serological, and radiological  data, whereas LASSO-LR and clustering techniques have been utilized in SLE diagnosis with an accuracy reached to 94.8% . Likewise, AI techniques recognized early AS with an accuracy of 91.8% . The PsA risk prediction models successfully predicted disease commencement up to four years before clinical diagnosis. Imaging techniques , such as X-rays, MRIs, and ultrasounds, incorporated with AI have  a proven efficacy in distinguishing structural damages and aids disease diagnosis .

Multi-omic approaches combining genomics, proteomics, and metabolomics promote tailored medicine, as represented in SSc and SjS, where specific or unique biomarker recognition and disease categorization have considerably improved. Despite these talented results, challenges still seen , including data shortage, models interpretability, and ethical issues regarding privacy and bias. High lightening  these problems is fundamental for AI's wide-ranging implementation in clinical practice. Future directions warrant  the establishment of multi-modal AI themes, mixing clinical, imaging, and molecular data, beside instituting concerted databases for training models. Real-time diagnostic techniques and AI-based decision-support systems are dignified to redefine autoimmune disease management. In conclusion, AI proposed a considerable prospect to facilitate the early recognition , diagnosis, and management of  various medical problems including autoimmune diseases.