Vollständiger Abstract
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Abstract Artificial intelligence (AI)-enabled infectious disease surveillance platforms are expanding rapidly, but their roles within health systems remain poorly characterized. This review examined 20 platforms according to primary data inputs, surveillance function, AI methods, data privacy, geographic scope and pathogen focus. Three archetypes emerged: Early Warning Networks, Situational Awareness Platforms and Integrated Surveillance Platforms. Their comparative analysis highlighted how data type, integration and localization shape platform contributions to public-health decision making.
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Publikationsdaten
- Autor:innen
- Marya Getchell, Michael Barber, Thomas Carpino, Carl J. E. Suster, Vitali Sintchenko, Nan Liu, Suci Wulandari, Yoong Khean Khoo, Ashley Tsai, Junxiong Pang, David B. Hipgrave, Shurendar Kumar, Ahmad Watsiq Maula, Philip AbdelMalik, Timothy J. Dallman, Paul M. Pronyk
- Quelle
- npj Digital Medicine
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2398-6352
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Zitierfähiger Nachweis
Marya Getchell, Michael Barber, Thomas Carpino, Carl J. E. Suster, Vitali Sintchenko, Nan Liu, Suci Wulandari, Yoong Khean Khoo, Ashley Tsai, Junxiong Pang, David B. Hipgrave, Shurendar Kumar, Ahmad Watsiq Maula, Philip AbdelMalik, Timothy J. Dallman, Paul M. Pronyk (2026). Platforms for artificial intelligence-enabled infectious disease surveillance. npj Digital Medicine. https://doi.org/10.1038/s41746-026-03189-x
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