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

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ID: 321513
2026
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Ranked #195 of 530 articles by views in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

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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.
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openalex_W7169628942 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jad Idriss, Suha Kalash, Jana Abu Faraj, L. Nolan, Kareem Bechnak, Nadine Rouphael
Journal Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Year 2026
DOI
10.1093/cid/ciag445
URL
https://doi.org/10.1093/cid/ciag445
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