scholarly journals Collaborating in the Time of COVID-19: The Scope and Scale of Innovative Responses to a Global Pandemic

10.2196/25935 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e25935
Author(s):  
Theresa Bernardo ◽  
Kurtis Edward Sobkowich ◽  
Russell Othmer Forrest ◽  
Luke Silva Stewart ◽  
Marcelo D'Agostino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Technology Policy ◽  
Vaccine Development ◽  
Contact Tracing ◽  
Quality Data ◽  
Data Sets ◽  
White House ◽  
Governmental Organizations ◽  
Global Pandemic ◽  
Data Propagation

The emergence of COVID-19 spurred the formation of myriad teams to tackle every conceivable aspect of the virus and thwart its spread. Enabled by global digital connectedness, collaboration has become a constant theme throughout the pandemic, resulting in the expedition of the scientific process (including vaccine development), rapid consolidation of global outbreak data and statistics, and experimentation with novel partnerships. To document the evolution of these collaborative efforts, the authors collected illustrative examples as the pandemic unfolded, supplemented with publications from the JMIR COVID-19 Special Issue. Over 60 projects rooted in collaboration are categorized into five main themes: knowledge dissemination, data propagation, crowdsourcing, artificial intelligence, and hardware design and development. They highlight the numerous ways that citizens, industry professionals, researchers, and academics have come together worldwide to consolidate information and produce products to combat the COVID-19 pandemic. Initially, researchers and citizen scientists scrambled to access quality data within an overwhelming quantity of information. As global curated data sets emerged, derivative works such as visualizations or models were developed that depended on consistent data and would fail when there were unanticipated changes. Crowdsourcing was used to collect and analyze data, aid in contact tracing, and produce personal protective equipment by sharing open designs for 3D printing. An international consortium of entrepreneurs and researchers created a ventilator based on an open-source design. A coalition of nongovernmental organizations and governmental organizations, led by the White House Office of Science and Technology Policy, created a shared open resource of over 200,000 research publications about COVID-19 and subsequently offered cash prizes for the best solutions to 17 key questions involving artificial intelligence. A thread of collaboration weaved throughout the pandemic response, which will shape future efforts. Novel partnerships will cross boundaries to create better processes, products, and solutions to consequential societal challenges.

scholarly journals Collaborating in the time of COVID-19: the scope and scale of innovative responses to a global pandemic (Preprint)

2020 ◽  
Author(s):  
Theresa Marie Bernardo ◽  
Kurtis Edward Sobkowich ◽  
Russell Othmer Forrest ◽  
Luke Stewart ◽  
Marcelo D'Agostino ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Small Businesses ◽  
Technology Policy ◽  
Vaccine Development ◽  
Nobel Laureate ◽  
Contact Tracing ◽  
Governmental Organizations ◽  
Data Propagation ◽  
Novel Coronavirus ◽  
Collaborative Efforts

UNSTRUCTURED Introduction: The emergence of COVID-19 spurred the formation of myriad teams to tackle every conceivable aspect of the virus and thwart its spread. Collaboration has become a constant theme throughout the 2019 novel coronavirus pandemic and has resulted in expedition of the scientific process (including vaccine development), rapid consolidation of global outbreak data and statistics, as well as experimentation with novel partnerships. Enabling these collaborative efforts is a state of global connectedness where data travels between countries in fractions of a second, allowing for partnerships and information sharing to occur virtually, with no need for physical proximity or even prior knowledge of your collaborators. The objective of this article is to document the evolution of these collaborative efforts, using illustrative examples collected by the authors throughout the pandemic and supplemented with publications from the JMIR COVID-19 Special Issue on coronavirus. Main Themes: Over 60 projects rooted in collaboration are categorized into five main themes: knowledge dissemination; data propagation; crowdsourcing; artificial intelligence; and hardware design and development. They highlight the numerous ways that citizens, industry professionals, researchers, and academics have come together globally to consolidate information and produce products geared towards combating the COVID-19 pandemic. With the overwhelming quantity of information, it can be challenging to gauge quality and detect misinformation, which is exacerbated by the inability to rapidly collect and share robust public health data. Initially, researchers and citizen scientists scrambled to pull together any accessible data. As global curated data sets started to emerge, numerous derivative works, such as visualizations or models, were developed that depended on the consistency of that data and which would fail when there were unanticipated changes. Crowdsourcing was used to collect and analyze data, aid in contact tracing, and to produce personal protective equipment (PPE) by sharing open designs for 3D printing. National and international consortia of entrepreneurs collaborated with researchers, including a Nobel Laureate, to create a ventilator that received rapid government approval and which was based on an open-source design. An equally impressive coalition of NGOs and governmental organizations led by the White House Office of Science and Technology Policy created a shared open resource of over 200,000 research publications about COVID-19 and subsequently challenged experts in artificial intelligence to answer 17 key questions, offering cash prizes for the best solutions. Conclusions: A thread of collaboration weaved throughout the pandemic response, which represents more than a series of random events. Thrust upon us, it will shape future efforts, pandemic or non-pandemic related. Novel partnerships, combining citizens, entrepreneurs, small businesses, corporations, academia, and governmental and non-governmental organizations will cross boundaries to create new processes, products and better solutions to consequential societal challenges.

Exploratory Insights on Artificial Intelligence for Government in Europe

2020 ◽  
pp. 089443932098044
Author(s):  
Colin van Noordt ◽  
Gianluca Misuraca
Keyword(s):  
Artificial Intelligence ◽  
Public Sector ◽  
Information And Communication Technologies ◽  
Communication Technologies ◽  
Quality Data ◽  
Comprehensive Overview ◽  
The Public ◽  
Governmental Organizations ◽  
Processing Power ◽  
Information And Communication

There is great interest to use artificial intelligence (AI) technologies to improve government processes and public services. However, the adoption of technologies has often been challenging for public administrations. In this article, the adoption of AI in governmental organizations has been researched as a form of information and communication technologies (ICT)–enabled governance innovation in the public sector. Based on findings from three cases of AI adoption in public sector organizations, this article shows strong similarities between the antecedents identified in previous academic literature and the factors contributing to the use of AI in government. The adoption of AI in government does not solely rely on having high-quality data but is facilitated by numerous environmental, organizational, and other factors that are strictly intertwined among each other. To address the specific nature of AI in government and the complexity of its adoption in the public sector, we thus propose a framework to provide a comprehensive overview of the key factors contributing to the successful adoption of AI systems, going beyond the narrow focus on data, processing power, and algorithm development often highlighted in the mainstream AI literature and policy discourse.

scholarly journals Applications and challenges of AI-based algorithms in the COVID-19 pandemic

2021 ◽  
Vol 7 (2) ◽  
pp. 387-398
Author(s):  
Danai Khemasuwan ◽  
Henri G Colt
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
Performance Metrics ◽  
Vaccine Development ◽  
Digital Transformation ◽  
Outbreak Detection ◽  
Contact Tracing ◽  
Real World Problem

The COVID-19 pandemic is shifting the digital transformation era into high gear. Artificial intelligence (AI) and, in particular, machine learning (ML) and deep learning (DL) are being applied on multiple fronts to overcome the pandemic. However, many obstacles prevent greater implementation of these innovative technologies in the clinical arena. The goal of this narrative review is to provide clinicians and other readers with an introduction to some of the concepts of AI and to describe how ML and DL algorithms are being used to respond to the COVID-19 pandemic. First, we describe the concept of AI and some of the requisites of ML and DL, including performance metrics of commonly used ML models. Next, we review some of the literature relevant to outbreak detection, contact tracing, forecasting an outbreak, detecting COVID-19 disease on medical imaging, prognostication and drug and vaccine development. Finally, we discuss major limitations and challenges pertaining to the implementation of AI to solve the real-world problem of the COVID-19 pandemic. Equipped with a greater understanding of this technology and AI’s limitations, clinicians may overcome challenges preventing more widespread applications in the clinical management of COVID-19 and future pandemics.

scholarly journals Mapping value sensitive design onto AI for social good principles

AI and Ethics ◽  
2021 ◽  
Author(s):  
Steven Umbrello ◽  
Ibo van de Poel
Keyword(s):  
Artificial Intelligence ◽  
Contact Tracing ◽  
Use Case ◽  
Value Sensitive Design ◽  
Specific Design ◽  
Established Method ◽  
Social Good ◽  
Do No Harm ◽  
Design Requirements ◽  
Whole Life Cycle

AbstractValue sensitive design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: (1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; (2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good, and (3) extending the VSD process to encompass the whole life cycle of an AI technology to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app.

scholarly journals TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 202
Author(s):  
Louai Alarabi ◽  
Saleh Basalamah ◽  
Abdeltawab Hendawi ◽  
Mohammed Abdalla
Keyword(s):  
Infectious Diseases ◽  
Infected Patient ◽  
Public Health Problem ◽  
Real Data ◽  
Exposure Period ◽  
Contact Tracing ◽  
Data Sets ◽  
Major Public Health Problem ◽  
Real Time Processing ◽  
Recent Developments

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.

scholarly journals Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 76
Author(s):  
Estrella Lucena-Sánchez ◽  
Guido Sciavicco ◽  
Ionel Eduard Stan
Keyword(s):  
Air Quality ◽  
Fundamental Problem ◽  
Multivariate Time Series ◽  
Quality Data ◽  
Data Sets ◽  
Air Quality Modelling ◽  
Regression Problem ◽  
Car Traffic ◽  
Pollutant Concentrations ◽  
Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

Artificial Intelligence and Ethics in Dentistry: A Scoping Review

2021 ◽  
pp. 002203452110138
Author(s):  
C.M. Mörch ◽  
S. Atsu ◽  
W. Cai ◽  
X. Li ◽  
S.A. Madathil ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Ethical Issues ◽  
Data Extraction ◽  
Cochrane Library ◽  
Data Sets ◽  
Ethical Challenges ◽  
Internal Validation ◽  
Neural Learning ◽  
Learning Techniques

Dentistry increasingly integrates artificial intelligence (AI) to help improve the current state of clinical dental practice. However, this revolutionary technological field raises various complex ethical challenges. The objective of this systematic scoping review is to document the current uses of AI in dentistry and the ethical concerns or challenges they imply. Three health care databases (MEDLINE [PubMed], SciVerse Scopus, and Cochrane Library) and 2 computer science databases (ArXiv, IEEE Xplore) were searched. After identifying 1,553 records, the documents were filtered, and a full-text screening was performed. In total, 178 studies were retained and analyzed by 8 researchers specialized in dentistry, AI, and ethics. The team used Covidence for data extraction and Dedoose for the identification of ethics-related information. PRISMA guidelines were followed. Among the included studies, 130 (73.0%) studies were published after 2016, and 93 (52.2%) were published in journals specialized in computer sciences. The technologies used were neural learning techniques for 75 (42.1%), traditional learning techniques for 76 (42.7%), or a combination of several technologies for 20 (11.2%). Overall, 7 countries contributed to 109 (61.2%) studies. A total of 53 different applications of AI in dentistry were identified, involving most dental specialties. The use of initial data sets for internal validation was reported in 152 (85.4%) studies. Forty-five ethical issues (related to the use AI in dentistry) were reported in 22 (12.4%) studies around 6 principles: prudence (10 times), equity (8), privacy (8), responsibility (6), democratic participation (4), and solidarity (4). The ratio of studies mentioning AI-related ethical issues has remained similar in the past years, showing that there is no increasing interest in the field of dentistry on this topic. This study confirms the growing presence of AI in dentistry and highlights a current lack of information on the ethical challenges surrounding its use. In addition, the scarcity of studies sharing their code could prevent future replications. The authors formulate recommendations to contribute to a more responsible use of AI technologies in dentistry.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

Coronaviruses: A Scientist’s Race Against Time

2020 ◽  
Author(s):  
Saloni Chaurasia ◽  
Keyword(s):  
Healthcare Workers ◽  
Vaccine Development ◽  
Drug Repurposing ◽  
Alveolar Epithelial Cells ◽  
Contact Tracing ◽  
Medical Innovation ◽  
Alveolar Epithelial ◽  
Treatment And Prevention ◽  
Scientific World ◽  
Respiratory Droplets

As the clock ticks, more and more people are falling victim to COVID-19, and scientists are racing against time to find treatment and prevention strategies. But what’s stopping them? The answer comes from two primary problems. Firstly, coronaviruses (CoVs) are transmitted from person-to-person via respiratory droplets from an infected person’s coughs or sneezes, which makes them highly contagious (CDC, How COVID-19 Spreads, 2020). This can happen in minutes, and up to 25% of patients remain asymptomatic (Du, et al., 2020). This makes it difficult for healthcare workers and researchers to contain patients and establish contact tracing to isolate the infected population. Secondly, it is hard to target CoVs without damaging our cells. CoVs infect via spike protein, which binds to the ACE2 receptor located on the lung alveolar epithelial cells (Hoffmann, et al., 2020). Once they invade the cell, CoVs hijack the host cell’s mechanisms to replicate. Thus, it is hard to combat the virus without damaging the host cell. On the other hand, recent understanding of CoVs structure and mechanism of action enables the scientific world to create a cure or vaccine. The bad news is that these efforts will likely face the perennial hurdles of medical innovation and discovery, long timelines of clinical trials for drug repurposing, and vaccine development, sometimes fickle funding, and changing governmental priorities.

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