The Role of Artificial Intelligence (AI) in Diagnosis, Treatment, and Prognosis of Neuroimmune Disorders

Background and aims: Neuroimmunological disorders refer to a group of diseases where theimmune system attacks the nervous system. These diseases can affect various parts of the nervoussystem, such as the central nervous system, peripheral nervous system, and neuromuscular junction.There is a increasing amount of neuroimmunological data available from clinical records,biological indicators, and imaging. Thus, it is crucial for clinicians to give more considerationto this information. Artificial intelligence (AI) can aid clinicians in analyzing and interpretingclinical data and images more effectively, leading to better decisions. AI-based processing on alarge scale can also help clinicians to evaluate their hypotheses. Additionally, AI can be utilizedto predict and detect neurological disorders. This article provides an overview of current researchadvancements in machine learning (ML) and deep learning (DL) methods for organizing big datain this field. It also discusses the challenges, options, and future directions for AI-based applicationsin the diagnosing, treating, and predicting the prognosis of neuroimmune disorders.Method: To find relevant studies, we searched through three databases: PubMed, Web of Science,and Scopus. Our search terms were “Artificial Intelligence”, “Neuroimmune Disorders”,“Machine learning”, and “deep learning”. We also manually searched through the reference listsof the studies we found to identify any additional relevant articles.Results: The technology of AI has great potential for processing clinical data and medical imaging.Most of the ML methods used in studies were linear regression, logistic regression, decisiontree, SVM algorithm, and random forest algorithm. The SVM algorithm is more effective in diagnosingdisease than other methods. Unfortunately, no symptoms, physical findings, or laboratorytests can accurately diagnose most neuroimmunological disorders. For this reason, several combinedapproaches are used to diagnose and manage, including reviewing the clinical data, medicalhistory, imaging such as MRI and CT scans, cerebrospinal fluid analysis, and blood tests. Whenresearchers use data from patients, non-uniformity is one of the important problems that theyhave, and some data, like MRI, are expensive on a large scale. In addition, ethical and privacyissues are associated with medical data sharing. To overcome these issues, researchers could useonline datasets such as ISBI ۲۰۱۵, MICCAI ۲۰۰۸, MICCAI ۲۰۱۶, and eHealth lab to apply theiralgorithms. For evaluation, AI models could test in cohort studies or hospitals under the supervisionof a clinician. ML algorithms increased the power of diagnosis significantly. However, acombination of knowledge from clinicians and ML algorithms yielded a higher predictive abilitythan ML alone and clinician alone when asked to diagnose and predict the prognosis.Conclusion: AI is widely used in healthcare to achieve various goals, such as disease detectionand prediction, drug and vaccine design, and personalized therapies. Early detection aided byAI-based techniques could increase patient’s survival. Furthermore, it is crucial to have a deeper understanding of the potential applications and limitations of AI-based methods to ensure theirsustainable and ethical implementation. This development is a positive step toward managingneuroimmunological diseases, and further research is necessary to expand AL-based methods inthe care of neuroimmunological diseases.

کلیدواژه ها:

Artificial Intelligence ، Neuroimmune Disorders ، personalized therapies ، predicting prognosis ، AI algorithms