Abstract

ADVANCES IN ARTIFICIAL INTELLIGENCE FOR DIAGNOSIS AND PHARMACOTHERAPEUTIC OPTIMIZATION OF AUTOIMMUNE DISORDERS

Gunavathi Ramu*, Harshini Chandramohan, Sanjana Saravanan, Viswanathan Kumar

Abstract

Autoimmune disorders, including systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis, are characterized by complex immunopathogenic mechanisms, heterogeneous phenotypes, and variable disease progression. Conventional diagnostic and therapeutic approaches often fail to address this complexity effectively. Artificial Intelligence (AI) offers a transformative approach by enabling data-driven, precision-based methodologies that surpass traditional strategies. Advanced AI models, such as deep learning and machine learning algorithms, facilitate the analysis of multiomics data, electronic health records, and pharmacological datasets, improving early diagnosis and disease prediction. AIdriven systems also enhance therapeutic decision-making by predicting drug responses, optimizing treatment regimens, and minimizing adverse effects. Furthermore, Natural Language Processing (NLP) enables extraction of clinically relevant insights from unstructured medical data, supporting pharmacovigilance. Despite its potential, challenges including interpretability, generalizability, and regulatory constraints remain. Overall, AI integration represents a promising advancement in the personalized management of autoimmune disorders.

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