The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.

The human leukocyte antigen (HLA) system underpins allorecognition and shapes the response to infection, autoimmunity, and treatment response. Technological advances from serology to next-generation sequencing now enable full-gene characterization and four-field HLA nomenclature, while artificial intelligence (AI) and machine learning are transforming the data generation, interpretation, and clinical use. This review summarizes the progress on the technical developments in the HLA era, which could be evaluated in three perspectives. First, we survey AI for antigen processing and T-cell recognition, including HLA–peptide binding, presentation, and T cell receptor (TCR)–epitope models, and outline their effects on applications like neoantigen discovery, vaccine design, and tolerance induction. Since there are still persistent gaps in immunogenicity prediction and coverage of rare alleles, secondly, we evaluated HLA imputation from the single nucleotide polymorphism (SNP) arrays and low-coverage whole-genome sequencing, highlighting deep learning models that improve accuracy for common and low-frequency alleles, and the critical role of diverse reference panels. Third, we assessed the AI-enabled transplant decision support: survival and graft-versus-host disease forecasting from registry data, donor ranking beyond simple allele match, and crossmatch compatibility prediction. We integrate emerging biology, non-classical HLA molecules, allele-specific expression, and HLA loss of heterozygosity, as key modulators of immune activation and evasion with implications for donor selection, infectious diseases, vaccinology, inflammatory disease, and cancer therapy. To accelerate safe clinical translation, we need to have standards for data governance, fairness auditing, validation and calibration, explainability, robustness, monitoring, and human oversight. By bridging core HLA principles with recent biological insights and AI innovations, we outline a path toward reproducible and equitable clinical translation to immunogenomics in transplantation, infectious, inflammatory, oncologic diseases, and precision vaccinology.