scholarly journals Use of artificial intelligence in infectious diseases

2020 ◽  
pp. 415-438 ◽  
Author(s):  
Said Agrebi ◽  
Anis Larbi
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases

scholarly journals Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review (Preprint)

2021 ◽  
Author(s):  
Ivy Y Zhao ◽  
Ye Xuan Ma ◽  
Man Wai Cecilia Yu ◽  
Jia Liu ◽  
Wei Nan Dong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Civil Rights ◽  
Infectious Diseases ◽  
Early Warning ◽  
Ethical Issues ◽  
Early Warning Systems ◽  
Surveillance Systems ◽  
Warning Systems ◽  
Digital Surveillance

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. CLINICALTRIAL PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180

scholarly journals Biomedical Applications with Using Embedded Systems

2021 ◽  
Author(s):  
Gulcicek Dere
Keyword(s):  
Artificial Intelligence ◽  
Embedded Systems ◽  
Infectious Diseases ◽  
Embedded System ◽  
Patient Management ◽  
The Other ◽  
System Structure ◽  
Portable Devices ◽  
The Embedded System ◽  
Commercial Applications

Besides the use of embedded systems in the field of electrical and electronics engineering, industrial, telecommunication, military, and many other commercial applications, and the other applications in the field of medical and biomedical are becoming increasingly common. Embedded system applications are increasing not only with designs on devices or with clothing, factories, medical and military equipments, portable devices, but also with applications such as ‘mobile worlds’ and ‘e-worlds’, Artificial Intelligence and IoT (Internet of things) with the possibility to make all kinds of software on them. In recent years, with the rise of infectious diseases such as the Covid 19 virus, there is a growing need for telemedicine applications such as diagnosis, prognosis and patient management. Embedded system technologies have occupied an important area in biomedical technology. Especially, to develop tools for the purposes of increasing the safety of healthcare workers in the event of epidemic infectious diseases in processes such as pandemics. For this purpose, monitoring of patients discharged from hospitals at home or non-intensive care beds during quarantine, or isolated in their homes, outpatient, and mildly ill, remotely, instantly, safely and quickly, are becoming increasingly important. In this section, we will give an overview of the embedded system structure and applications.

scholarly journals A Testbed for Studying COVID-19 Spreading in Ride-Sharing Systems

2020 ◽  
Author(s):  
Harrison Jun Yong Wong ◽  
Zichao Deng ◽  
Han Yu ◽  
Jianqiang Huang ◽  
Cyril Leung ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Interaction Effect ◽  
Public Transport ◽  
Transport Network ◽  
Complex Environment ◽  
Ride Sharing ◽  
Multi Agent

Order dispatch is an important area where artificial intelligence (AI) can benefit ride-sharing systems (e.g., Grab, Uber), which has become an integral part of our public transport network. In this paper, we present a multi-agent testbed to study the spread of infectious diseases through such a system. It allows users to vary the parameters of the disease and behaviours to study the interaction effect between technology, disease and people's behaviours in such a complex environment.

scholarly journals Artificial Intelligence-based Disease Surveillance Amid COVID-19 and Beyond: A Systematic Review Protocol

2020 ◽  
Author(s):  
Zhaohui Su ◽  
Barry Bentley ◽  
Feng Shi
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Disease Surveillance ◽  
Cost Effective ◽  
Surveillance Systems ◽  
Prevention Measures ◽  
Eligibility Criteria ◽  
The World ◽  
Tools And Techniques ◽  
Health Experts

Abstract Background: Infectious diseases are dangerous and deadly. As the leading causes of morbidity and mortality in all demographics across the world, infectious diseases carry substantial social, economic, and healthcare costs. Unlike previous global health crises, health experts now have access to more advanced tools and techniques to understand pandemics like COVID-19 better and faster; one such class of tools is artificial intelligence (AI) enabled disease surveillance systems. AI-based surveillance systems allow health experts to perform rapid mass infection prediction to identify potential disease cases, which is integral to understanding transmission and curbing the spread of the pandemic. However, while the importance of AI-based disease surveillance mechanisms in pandemic control is clear, what is less known is the state-of-the-art application of these mechanisms in countries across the world. Therefore, to bridge this gap, we aim to systematically review the literature to identify (1) how AI-based disease surveillance systems have been applied in counties worldwide amid COVID-19, (2) the characteristics and effects of these applications regarding the control of the spread of COVID-19, and (3) what additional disease surveillance resources such as database, AI-based tools and techniques that can be further added to the current toolbox in the fight against COVID-19. Methods: To locate research on AI-based disease surveillance amid COVID-19, we will search databases including PubMed, IEEE Explore, ACM Digital Library, and Science Direct to identify all potential records. Titles, abstracts, and full-text articles were screened against eligibility criteria developed a priori. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures was adopted as the reporting framework.Results: NA for now Conclusions: Findings of our study will fill an important void in the literature, as no research has systematically reviewed available AI-based disease surveillance in the context of COVID-19. As the world continues to reel from COVID-19, it is important to identify cost-effective AI-based disease surveillance mechanisms that can detect COVID-19 cases and explain how one COVID-19 case turns into many cases, so that better prevention measures can be established to curb the spread of the COVID pandemic in a timely manner. Study Protocol Registration: PROSPERO CRD42020204992

scholarly journals Interim evaluation of Google AI forecasting for COVID-19 compared with statistical forecasting by human intelligence in the first week

2020 ◽  
Author(s):  
Junko Kurita ◽  
Tamie Sugawara ◽  
Yasushi Ohkusa
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Statistical Model ◽  
Onset Date ◽  
Human Intelligence ◽  
Epidemic Curve ◽  
Number Of Patients ◽  
The Usa ◽  
Discrepancy Rate ◽  
Traditional Surveillance

AbstractBackgroundSince June, Google (Alphabet Inc.) has provided forecasting for COVID-19 outbreak by artificial intelligence (AI) in the USA. In Japan, they provided similar services from November, 2020.ObjectWe compared Google AI forecasting with a statistical model by human intelligence.MethodWe regressed the number of patients whose onset date was day t on the number of patients whose past onset date was 14 days prior, with information about traditional surveillance data for common pediatric infectious diseases including influenza, and prescription surveillance 7 days prior. We predicted the number of onset patients for 7 days, prospectively. Finally, we compared the result with Googles AI-produced forecast. We used the discrepancy rate to evaluate the precision of prediction: the sum of absolute differences between data and prediction divided by the aggregate of data.ResultsWe found Google prediction significantly negative correlated with the actual observed data, but our model slightly correlated but not significant. Moreover, discrepancy rate of Google prediction was 27.7% for the first week. The discrepancy rate of our model was only 3.47%.Discussion and ConclusionResults show Googles prediction has negatively correlated and greater difference with the data than our results. Nevertheless, it is noteworthy that this result is tentative: the epidemic curve showing newly onset patients was not fixed.

scholarly journals Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases

2021 ◽  
Author(s):  
Adam Bess ◽  
Frej Berglind ◽  
Supratik Mukhopadhyay ◽  
Michal Brylinski ◽  
Nicholas Griggs ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Diseases ◽  
Antimicrobial Agents ◽  
Emerging Infectious Diseases
Sign in / Sign up

Export Citation Format

Share Document