The Current and Future of Machine Learning and Deep Learning in the Treatment of Autoimmune Diseases

Autoimmune illnesses pose formidable diagnostic and prognostic
obstacles due to the wide range of symptoms they cause and the
immune system's tendency to malfunction, which in turn causes the
creation of autoantibodies. Although early diagnosis and personalized
treatment are of the utmost importance, traditional approaches
sometimes lack predictive power. Through the analysis of massive
datasets and the creation of sophisticated diagnostic and prediction
tools, machine learning (ML) presents a promising approach to
addressing these challenges. protocols for autoimmune diseases
affecting several organs and systems (e.g., rheumatoid arthritis, sle,
lupus erythematosus). (The autoimmune thyroid disease, gastrointestinal
disorders, skin diseases, and type 1 diabetes mellitus are all examples).
The growing promise of machine learning algorithms for issue predicting,
therapeutic response evaluation, and early disease detection is
highlighted by our work. To go a step further, we look at how ongoing
research and the addition of more varied and extensive datasets might
improve these models' accuracy and dependability. This will enable
healthcare providers to detect autoimmune diseases at an early stage
and guide the creation of efficient treatment strategies.