Background: The COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a "double-edge sword," as the ethical governance of such approaches often lags behind technological achievements.
Objective: The aim was to investigate ethical issues identified from utilizing artificial intelligence-augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks.
Methods: In a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework.
Results: This systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients' highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights.
Conclusions: Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure.
Trial registration: PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180.
Keywords: artificial intelligence; electronic medical records; ethics; infectious diseases; machine learning.
©Ivy Y Zhao, Ye Xuan Ma, Man Wai Cecilia Yu, Jia Liu, Wei Nan Dong, Qin Pang, Xiao Qin Lu, Alex Molassiotis, Eleanor Holroyd, Chi Wai William Wong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2021.