Artificial intelligence and machine learning–driven advances in autoimmune diseases

Autoimmune diseases—including vitiligo, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes, rheumatoid arthritis, primary biliary cirrhosis and primary Sjögren’s Syndrome represent a major and growing health burden worldwide. These disorders arise from a breakdown of immune tolerance, leading to the activation of autoreactive lymphocytes and chronic tissue damage. Despite substantive advances in cellular immunology and molecular genetics, the clinical translation of this knowledge remains constrained by the profound heterogeneity, pleiotropy, and stochasticity inherent in autoimmune pathogenesis.In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in biomedical research. By leveraging sophisticated computational architectures, AI affords the capacity to interrogate high-dimensional, multimodal datasets— including multi-omics (genomic, transcriptomic, proteomic, metabolomic), spatially resolved single-cell analyses, high-resolution imaging modalities, and large-scale electronic health records—with a granularity hitherto unattainable through conventional statistical methodologies. In the context of autoimmunity, AI applications hold the potential to:• Advance mechanistic understanding by identifying immune cell subsets, signalling networks, and regulatory pathways that drive disease initiation and progression.• Improve diagnostics and classification by discovering computational biomarkers, refining disease subtypes, and predicting individual risk profiles.• Transform therapy and precision medicine by forecasting treatment response, guiding therapeutic choices, and identifying new targets or repurposed drugs.This Research Topic brings together immunologists, computational scientists, and clinicians to explore the emerging role of AI in autoimmune disease research and clinical management. We welcome original research, reviews, perspectives, and opinion pieces that highlight innovative approaches and interdisciplinary collaborations.Areas of focus include, but are not limited to:• AI-driven analysis of immune tolerance and checkpoint regulation in autoimmune milieus.• Integration of single-cell and spatial multi-omics data with machine learning approaches for autoimmune disease stratification.• Predictive modeling of autoimmune disease progression and therapy response.• Algorithmic innovations for multimodal biomarker discovery in autoimmune disorders.• Ethical, regulatory, and translational challenges in implementing AI in clinical immunology.By convening a diverse group of researchers and clinicians, this Research Topic will create a platform to accelerate discovery, foster collaboration, and inspire novel therapeutic strategies.We anticipate that the collection will not only advance mechanistic understanding of autoimmunity but also pave the way for clinically actionable AI applications, bridging the gap between bench, bedside, and computational innovation. Topic editors: Taruna Madan, Ken Okamura Source: https://www.frontiersin.org/research-topics/74040/artificial-intelligence-and-machine-learning-driven-advances-in-autoimmune-diseases