AI-Enhanced Diagnosis for Immunological Disorders

Abstract

The integration of Artificial Intelligence (AI) in immunological disorder diagnosis is transforming medical diagnostics by enabling rapid and accurate decision-making. This chapter explores AI-enhanced techniques, including machine learning, deep learning, and natural language processing, in identifying disease biomarkers, predicting progression, and optimizing personalized treatments. Key methodologies such as convolutional neural networks for imaging, support vector machines for classification, and AI-assisted genomic analysis are discussed. Additionally, the role of big data, electronic health records, and federated learning in improving diagnostic accuracy is examined. While AI offers significant advancements, challenges like data heterogeneity, bias, and regulatory compliance remain. The chapter highlights AI's future potential in precision medicine, enhancing early disease detection and improving patient outcomes.