{"id":16892,"date":"2026-07-12T13:01:00","date_gmt":"2026-07-12T05:01:00","guid":{"rendered":"https:\/\/vaxlab.dukekunshan.edu.cn\/?post_type=project-news&p=16892"},"modified":"2026-09-08T16:43:30","modified_gmt":"2026-09-08T08:43:30","slug":"applications-and-current-limitations-of-artificial-intelligence-across-the-lifespan-of-vaccines","status":"publish","type":"project-news","link":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/project-news\/applications-and-current-limitations-of-artificial-intelligence-across-the-lifespan-of-vaccines\/","title":{"rendered":"Applications and Current Limitations of Artificial Intelligence Across the Lifespan of Vaccines"},"content":{"rendered":"\n

A 2026 systematic review in Current Research in Biotechnology<\/em> documents that artificial intelligence and machine learning (AI\/ML) now span the full vaccine lifecycle \u2014 antigen discovery, epitope prediction, candidate screening, preclinical and clinical development, manufacturing optimization, demand forecasting, cold-chain management, and equitable deployment. The evidence base is expanding rapidly, but three structural constraints, including data silos, algorithmic bias, and uneven validation across populations and settings, continue to limit translation of AI capability into measurable health gains.<\/p>\n\n\n\n

In discovery and development, convolutional neural networks, support vector machines, and ensemble methods are widely applied to epitope prediction, antigen classification, and immunogen design, including for SARS-CoV-2, hepatitis C, Zika, and malaria targets. Structure-prediction tools such as AlphaFold2 provide conformational information that accelerates evaluation of epitope exposure and structural stability for subunit, mRNA, and multi-epitope vaccine design. In clinical development, AI\/ML supports immune-response prediction, trial optimization, and adverse-event prediction. However, model generalizability remains constrained by reliance on curated datasets, with performance often degrading when extrapolated to new populations. Predicted immunogenicity is not equivalent to demonstrated protection, and in-silico accuracy does not guarantee real-world translatability\u2014experimental and clinical validation remain indispensable.<\/p>\n\n\n\n

On the delivery side, immunization shortfalls are frequently driven by delivery and implementation failures rather than vaccine unavailability. AI-driven predictive analytics, integrating epidemiological trends, demographic patterns, seasonality, and historical uptake, enable dynamic demand forecasting that reduces stockouts and wastage relative to static, historical-average allocation. AI-enabled IoT systems support continuous cold-chain monitoring, with smart sensors predicting equipment failure and triggering maintenance or rerouting before potency loss occurs. AI is also applied to post-marketing safety surveillance, though interpretability is essential in this context: statistical association cannot substitute for causal inference, and model outputs must specify key variables, applicable populations, and error margins. A critical limitation is that many low-resource settings lack the technical infrastructure to fully leverage these systems, risking a widening gap in distribution efficiency across regions.<\/p>\n\n\n\n

AI also has a demonstrated role in advancing equity. Using Pakistan Demographic and Health Survey data (7,150 records, 31 sociodemographic attributes), a LightGBM model classified vaccine acceptance into four WHO-coverage-aligned tiers, achieving 98% accuracy on a balanced dataset. However, AI training data drawn predominantly from well-resourced settings risks encoding existing inequities as “normal,” thereby underestimating true need in underserved populations.<\/p>\n\n\n\n

At the governance level, data fragmentation across R&D, clinical trials, pharmacovigilance, manufacturing, cold-chain, and immunization registries, compounded by inconsistent formats and the absence of standardized, interoperable metadata, constrains cross-regional evidence integration. Algorithmic bias arising from unrepresentative or imbalanced training data warrants stratified performance evaluation before deployment, particularly for models informing prioritization or resource allocation. Real-world validation remains limited, with many models confined to simulation or single-dataset evaluation.<\/p>\n\n\n\n

Public health adoption should be conditioned on demonstrated impact on timeliness, waste reduction, coverage equity, workforce burden, and cost-effectiveness, not on performance metrics alone. Explainable AI, implementation science, and health-economic evaluation should become standard components of vaccine AI research going forward.<\/p>\n\n\n\n

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Relevant Research:<\/p>\n\n\n\n

[1] Okesanya OJ, Hassan MM, Adebayo UO, et al. Artificial intelligence and machine learning across vaccine lifecycle: a systematic review. Current Research in Biotechnology. 2026;11:100379.<\/a><\/p>\n\n\n\n

[2] Qureshi MS, Qureshi MB, Iqrar U, et al. AI based predictive acceptability model for effective vaccine delivery in healthcare systems. Scientific Reports. 2024;14:26657.<\/a><\/p>\n\n\n\n

[3] Next Global Health Consulting. AI Solutions for Supply Chain Optimization in Global Vaccine Distribution. 2025.<\/p>\n\n\n\n

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Translating and summarizing: Tianyi Deng<\/p>\n\n\n\n

Layout editing by: Ruitong Li<\/p>\n","protected":false},"excerpt":{"rendered":"

A 2026 systematic review in Current Research in Biotechnology documents that artificial intelligence and machine learning (AI\/ML) now span the full vaccine lifecycle \u2014 antigen discovery, epitope prediction, candidate screening, preclinical and clinical development, manufacturing optimization, demand forecasting, cold-chain management, and equitable deployment. The evidence base is expanding rapidly, but three structural constraints, including data silos, […]<\/p>\n","protected":false},"featured_media":16748,"menu_order":0,"template":"","meta":{"type_label":"none_selected"},"news-category":[73],"class_list":["post-16892","project-news","type-project-news","status-publish","has-post-thumbnail","hentry","news-category-research-en"],"_links":{"self":[{"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/project-news\/16892","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/project-news"}],"about":[{"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/types\/project-news"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/media\/16748"}],"wp:attachment":[{"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/media?parent=16892"}],"wp:term":[{"taxonomy":"news-category","embeddable":true,"href":"https:\/\/vaxlab.dukekunshan.edu.cn\/en\/wp-json\/wp\/v2\/news-category?post=16892"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}