ArticleFrontiers in public health2026
Analysis of an AI-powered system for vaccination screening, monitoring, and management in adults aged 50 and above.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Currently, there is insufficient research on screening contraindications and post-vaccination monitoring for older adults prior to vaccination. Methods: This study pioneers the integration of artificial intelligence technology, developing a mobile-based medical system (Medduo) that leverages AI for the screening and monitoring of vaccinations in Older adults with chronic diseases. The system was piloted at five vaccination centers. It first assessed residents' health status and used AI algorithms to recommend appropriate vaccines based on their responses to programmed questions. Personalized suggestions were delivered through mobile terminals. The study compared suspected adverse reactions monitoring by age and gender. Results: The study data were derived from 2,609 individuals aged 50 and above, of whom 2,599 completed pre-vaccination health screening via mobile terminals. The participants had high rates of previous COVID-19 and influenza vaccinations, at 80.68 and 91.30%, respectively, and 23-valent pneumococcal vaccine and varicella-zoster vaccine vaccination rates of 33.69 and 10.58%, respectively. Most participants had previously been infected with the novel coronavirus, with an infection rate of 74.93%. Analysis of AEFI reports across various age groups showed that the overall incidence of AEFI was 7.83% (207/2,645, equivalent to 7,826.09 per 100,000 population), with the highest report rate observed among those aged 50-59, reaching statistical significance. Discussion: This self-developed system effectively screened for contraindications in individuals aged 50 or older through intelligent means, reducing the time cost of traditional pre-vaccination screening, and collected AEFI data through a combination of active and passive monitoring, with high sensitivity, contributing to digital health implementation in immunization programs.
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