review · Frontiers in Immunology
Artificial intelligence plays a pivotal role in speeding up vaccine development, improving vaccine efficacy and safety, and strengthening public acceptance. However, fully achieving these advantages depends on targeted investments in infrastructure and meaningful stakeholder engagement. Responsible adoption also calls for transparent documentation of models, routine audits for algorithmic bias, and interdisciplinary ethical oversight. Furthermore, translating computational predictions into tangible real-world outcomes requires large-scale validation studies alongside analytical methods capable of handling heterogeneous forms of evidence. Addressing these technical, ethical, and practical requirements is essential for ensuring that artificial intelligence innovations contribute to equitable global health solutions and bolster future pandemic preparedness.
Harnessing artificial intelligence can drastically shorten the timeline for creating safe and effective vaccines during health emergencies. However, algorithmic tools require rigorous validation and ethical safeguards to ensure they work reliably across diverse populations, ultimately supporting public trust and strengthening global readiness for future disease outbreaks.
The findings are relevant to vaccine developers, biopharmaceutical organisations, and public health agencies integrating computational tools into drug discovery pipelines. Current development appears to be at an early to intermediate translation stage, moving from computational models toward clinical application, with widespread deployment still contingent on large-scale validation studies, infrastructure investments, and established bias auditing frameworks.
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This umbrella review confirms AI's pivotal role in accelerating vaccine development, enhancing efficacy and safety, and bolstering public acceptance. Realizing these benefits requires not only investments in infrastructure and stakeholder engagement but also transparent model documentation, interdisciplinary ethics oversight, and routine algorithmic bias audits. Moreover, bridging the gap from in silico promise to real‑world impact demands large‑scale validation studies and methods that can accommodate heterogeneous evidence, ensuring AI‑driven innovations deliver equitable global health outcomes and reinforce pandemic preparedness.
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DOI: 10.3389/fimmu.2025.1567116
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