Artificial Intelligence Across the Vaccine Clinical Trial Lifecycle: Evidence, Readiness, and Guardrails.

Abstract

Artificial intelligence (AI) is increasingly being used to support clinical research, but its value in vaccine clinical trials requires careful evidence-based assessment. Vaccine trials pose distinctive challenges, including high safety expectations in healthy participants, evolving pathogen exposure and baseline immunity, incomplete correlates of protection, applicability of findings to intended-use populations, and intense public scrutiny. We conducted a structured, vaccine-focused narrative review of AI applications across the vaccine trial lifecycle, supplemented by targeted clinical trial and vaccine pharmacovigilance studies with directly transferable methods. In the combined evidence base, evidence is strongest for operational uses, particularly recruitment, eligibility screening, trial matching, and risk-based monitoring. Applications to immune-response interpretation, correlates of protection, and vaccine safety surveillance are promising but remain less prospectively validated. Responsible adoption should be guided by intended tool use, evidence of strength, data governance, regulatory expectations, and preservation of human scientific and safety judgment.