ReviewFrontiers in public health2026
Artificial intelligence in public health education: current evidence, competencies, and a framework for responsible integration.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Public health practice and education are being profoundly transformed by artificial intelligence (AI), yet formal AI literacy training in academic programs remains lacking. This narrative review synthesizes literature identified through a structured search of PubMed, Scopus, Web of Science, and Google Scholar for publications issued between January 2020 and June 2026. The narrative synthesis draws on 21 publications, including empirical studies, reviews, perspectives, educational frameworks, and authoritative guidance documents. The review underscores a significant gap between the widespread use of informal AI and the limited availability of formal curricula, with most evidence drawn from medical education rather than public health-specific settings. Key applications include adaptive learning platforms, AI-enhanced simulation, and research training support. We propose the Public Health AI-Education Integration Framework comprising nine domains: learner and curriculum needs assessment; AI literacy foundation; AI tool selection and educational alignment; public health dataset and case integration; adaptive learning and simulation delivery; human-in-the-loop supervision; bias, equity, and ethical auditing; assessment and academic integrity; and feedback, monitoring, and curriculum refinement. The framework is proposed as a conceptual model requiring prospective validation, intended to move beyond
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.