Browse by author
Lookup NU author(s): Professor Cristina NeeshamORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnectedworld. In the last 20 years, resistant infectious diseases such as severe acute respiratory syndrome(SARS), Middle East respiratory syndrome (MERS), H1N1 influenza (swine flu), Ebola virus, Zikavirus, and now COVID-19 have been impacting global health defences, and aggressively flourishingwith the rise of global travel, urbanization, climate change, and ecological degradation. In parallel,this extraordinary episode in global human health highlights the potential for artificial intelligence(AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-timedata to inform epidemiological and public health emergency responses. The uses of AI in thesedynamic environments are increasingly complex, challenging the potential for human autonomousdecisions. In this context, our study of qualitative perspectives will consider a responsible AIframework to explore its potential application to disease surveillance in a global health context. Thusfar, there is a gap in the literature in considering these multiple and interconnected levels of diseasesurveillance and emergency health management through the lens of a responsible AI framework.
Author(s): Borda A, Molnar A, Neesham C, Kostkova P
Publication type: Article
Publication status: Published
Journal: Applied Sciences
Year: 2022
Volume: 12
Issue: 8
Online publication date: 12/04/2022
Acceptance date: 08/04/2022
Date deposited: 29/04/2022
ISSN (electronic): 2076-3417
Publisher: MDPI AG
URL: https://doi.org/10.3390/app12083890
DOI: 10.3390/app12083890
Altmetrics provided by Altmetric