Harnessing artificial intelligence and digital technology for enhancing routine immunization among zero-dose children

"Zero-dose children" remain a crucial burden to global health, particularly in low- and middle-income countries (LMICs) where vulnerable children have limited access to immunizations. This review explores the transformative potential of artificial intelligence (AI) in addressing this challenge and examines how AI can enhance routine childhood immunization.

This narrative review synthesizes literature on AI, digital health innovations, vaccine delivery, and public health informatics. Literature was sourced from PubMed, Scopus, and Google Scholar, as well as institutional and authoritative reports such as the World Health Organization, the United Nations Children's Fund (UNICEF), and Gavi, the Vaccine Alliance.

AI applications have demonstrated utility in identifying zero-dose populations, optimizing vaccine delivery, and supporting data-driven decision-making. Tools such as digital health passports, predictive analytics, and real-time monitoring platforms (e.g. UNICEF's real-time vaccination monitoring and analysis) enhance immunization tracking and reduce inequities. AI-driven chatbots, mobile applications, and social listening tools address vaccine hesitancy by providing tailored communication and combating misinformation. Furthermore, geographic information systems (GISs) and AI-based behavioral analysis improve outreach to underserved populations. However, challenges remain, including data quality, scalability, and ethical considerations.

AI solutions allow efficient identification of potential pockets of non-vaccinated populations, enhance decision-making processes based on data, and reduce vaccine skepticism due to AI-based interventions. Examples of successful implementation of AI include digital health passports, continuous monitoring mechanisms, and GISs for vaccine administration and coverage. This review also discusses the challenges and ethical considerations associated with AI implementation in LMICs, as well as the ethical implications. Using the insights derived from the review, this paper calls for the targeted utilization of AI toward the realization of a healthier future for children in need to achieve the target of reducing the number of zero-dose children by 50% by the year 2030, not only in areas afflicted by immunization disparities but across the globe.