Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and...

Type 1 diabetes (T1D) is a chronic autoimmune disorder characterized by immune-mediated destruction of pancreatic β-cells, resulting in absolute insulin deficiency and lifelong dependence on exogenous insulin therapy. As a chronic condition with substantial population-level clinical, psychosocial, and economic consequences, T1D represents an important public health challenge that extends beyond individual glycemic management. Although major advances in insulin formulations, continuous glucose monitoring systems, and automated insulin delivery technologies have substantially improved glycemic control and reduced complications, these approaches remain fundamentally disease-management strategies and do not address the underlying autoimmune pathology or restore durable endogenous insulin secretion. Over the past several decades, multiple clinical trials targeting immune modulation, β-cell preservation, transplantation, and metabolic pathways have generated significant mechanistic insights but have rarely achieved sustained insulin independence or long-term disease remission. Progress toward disease-modifying and prevention-oriented therapies has been hindered by biological heterogeneity, limited mechanistic biomarkers, translational gaps between experimental models and human disease, and fragmented data ecosystems. Advances in artificial intelligence (AI) and machine learning (ML) enable integrative analysis of multi-omics, clinical, and digital health datasets to define disease endotypes, predict disease trajectories, and guide the rational design of combination therapies and adaptive clinical trials. Within a digital public health framework, these approaches may also support earlier risk stratification, improved therapeutic targeting, more efficient trial design, and scalable implementation across diverse populations. However, the clinical translation of AI in T1D remains constrained by heterogeneity and fragmentation across available datasets, limited external validation, and inadequate representation of demographically and clinically diverse populations. Additional barriers include data privacy and governance requirements, the risk of algorithmic bias, and the need for interpretable and reproducible AI models that can meet rigorous clinical, ethical, and regulatory standards. This review synthesizes current knowledge of T1D pathophysiology and critically examines historical limitations in therapeutic development while proposing an AI-enabled precision medicine framework that integrates immune tolerance induction, β-cell restoration strategies, and metabolic optimization. Finally, we outline a translational roadmap for data-driven therapeutic discovery and adaptive clinical development aimed at accelerating progress toward durable, scalable, equitable, and biologically informed interventions for T1D with relevance to public health practice, prevention, and population health outcomes.