ArticleFrontiers in digital health2026
Artificial intelligence is transforming disease surveillance, but governance is failing to keep pace.
Article in Frontiers in digital 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.
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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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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.
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Authors and funding
5 authors.
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Abstract
Artificial intelligence (AI) is transforming disease surveillance by enabling early outbreak detection, predictive modeling, and real-time analysis of diverse health data sources. These advances can strengthen epidemic preparedness and improve public health decision-making. However, AI adoption has progressed faster than the development of effective governance frameworks, creating challenges related to algorithmic bias, transparency, privacy, cybersecurity, accountability, and global equity. Low- and middle-income countries face additional barriers due to limited digital infrastructure and technical capacity. This policy brief highlights the urgent need for responsible AI governance through international standards, explainable algorithms, independent validation, strong data protection, and inclusive stakeholder engagement. Ensuring ethical and equitable AI implementation is essential to maximize its potential for improving disease surveillance and global health security.
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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.