scholarly journals Healthcare Applications of Artificial Intelligence and Analytics: A Review and Proposed Framework

2020 ◽  
Vol 10 (18) ◽  
pp. 6553
Author(s):  
Sabrina Azzi ◽  
Stéphane Gagnon ◽  
Alex Ramirez ◽  
Gregory Richards
Keyword(s):  
Artificial Intelligence ◽  
Chronic Care ◽  
Review Paper ◽  
Research Community ◽  
Patient Needs ◽  
Care Needs ◽  
Healthcare Applications ◽  
Medical Specialties ◽  
The Past ◽  
Promising Application

Healthcare is considered as one of the most promising application areas for artificial intelligence and analytics (AIA) just after the emergence of the latter. AI combined to analytics technologies is increasingly changing medical practice and healthcare in an impressive way using efficient algorithms from various branches of information technology (IT). Indeed, numerous works are published every year in several universities and innovation centers worldwide, but there are concerns about progress in their effective success. There are growing examples of AIA being implemented in healthcare with promising results. This review paper summarizes the past 5 years of healthcare applications of AIA, across different techniques and medical specialties, and discusses the current issues and challenges, related to this revolutionary technology. A total of 24,782 articles were identified. The aim of this paper is to provide the research community with the necessary background to push this field even further and propose a framework that will help integrate diverse AIA technologies around patient needs in various healthcare contexts, especially for chronic care patients, who present the most complex comorbidities and care needs.

scholarly journals Artificial Intelligence along the New Silk Road

2020 ◽  
pp. 240-254
Author(s):  
Lynda Hardman
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
International Collaboration ◽  
Research Community ◽  
Current Situation ◽  
Silk Road ◽  
Diplomatic Relations ◽  
The Past ◽  
The Usa

Chapter 13 gives an impression of the development of the relatively young AI and computer science fields in Europe and China and how the current situation has developed over the past twenty years, where European and Chinese researchers are equal colleagues on an international stage and where diplomatic relations between the USA and China on the international stage have consequences felt directly by European AI researchers in their labs. In what ways are AI researchers in China and Europe competitors with each other, for example in terms of the global shortage of trained AI researchers and practitioners? At the same time, the AI research community collaborates globally, so how can we ensure that the field continues to benefit from open international collaboration?

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

2021 ◽  
Vol 2 (2) ◽  
pp. 1-15
Author(s):  
Shruti Agarwal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Engineering ◽  
Brain Mri ◽  
Research Community ◽  
Research Subject ◽  
Engineering Research ◽  
Artificial Intelligence Techniques ◽  
The Past ◽  
History Of ◽  
Medical Domain

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.

scholarly journals Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jorge Humberto Moreno-Scott ◽  
José Carlos Ortiz-Bayliss ◽  
Hugo Terashima-Marín ◽  
Santiago Enrique Conant-Pablos
Keyword(s):  
Artificial Intelligence ◽  
Constraint Satisfaction ◽  
Operations Research ◽  
Special Interest ◽  
Research Community ◽  
Constraint Satisfaction Problems ◽  
The Past ◽  
Variable Ordering ◽  
Instance Space ◽  
Variable Ordering Heuristics

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

The Digital Divide: Addressing Artificial Intelligence in Communication Education

2020 ◽  
Vol 75 (4) ◽  
pp. 470-482
Author(s):  
Regina Luttrell ◽  
Adrienne Wallace ◽  
Christopher McCollough ◽  
Jiyoung Lee
Keyword(s):  
Artificial Intelligence ◽  
Digital Divide ◽  
Digital Media ◽  
Research Community ◽  
Communication Education ◽  
The Past ◽  
The Media ◽  
Media Research ◽  
Media And Communication ◽  
Exhaustive List

Artificial intelligence (AI) has gained both momentum and importance within society over the past several years. This article provides an opening for further discussion to the broader social and digital media research community and those interested in answering important questions related to these areas by leveraging a focused, productive approach. In supporting future educational endeavors within the communication classroom, and specifically to this topic, we propose five important considerations that will move the conversation forward. The considerations within this article are meant to engage scholars in intellectual conversation and to provide an initial foundation for the direction of communication education. They are not meant to be an exhaustive list, but rather initiate discussions within education and research addressing implications emerging technologies have had on our field and what could be incorporated into the media and communication curriculum to prepare educators and students alike.

scholarly journals Ten years of liquid democracy research

2020 ◽  
Vol 338 ◽  
pp. 455-466
Author(s):  
Alois Paulin
Keyword(s):  
Artificial Intelligence ◽  
Research Community ◽  
The Past ◽  
Liquid Democracy ◽  
Technical Systems

Ten years have passed since in 2009 two independent technical systems pioneered liquid democracy (LD): Župa, a system developed for the Student Organisation of FIS in Slovenia, followed closely by LiquidFeedback, which served the Pirate Party in Berlin, Germany. First academic papers appeared in 2010 targeting the e-democracy research community, and later followed by researchers from artificial intelligence. This paper provides an overview of the scholarly discussions that developed in the past decade, and the technical implementations and initiatives that emerged.

Novel indicator for the assessment of hospital bed occupancy

2011 ◽  
Vol 152 (20) ◽  
pp. 797-801 ◽  
Author(s):  
Miklós Gresz
Keyword(s):  
Patient Needs ◽  
Bed Occupancy ◽  
The Past ◽  
Hospital Beds ◽  
Hospital Bed ◽  
Bed Capacity ◽  
Statistical Indicators

In the past decades the bed occupancy of hospitals in Hungary has been calculated from the average of in-patient days and the number of beds during a given period of time. This is the only measure being currently looked at when evaluating the performance of hospitals and changing their bed capacity. The author outlines how limited is the use of this indicator and what other statistical indicators may characterize the occupancy of hospital beds. Since adjustment of capacity to patient needs becomes increasingly important, it is essential to find indicator(s) that can be easily applied in practice and can assist medical personal and funders who do not work with statistics. Author recommends the use of daily bed occupancy as a base for all these statistical indicators. Orv. Hetil., 2011, 152, 797–801.

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

Evaluating Qualitative Research

2017 ◽  
Author(s):  
Jeasik Cho
Keyword(s):  
Qualitative Research ◽  
Large Scale ◽  
Academic Research ◽  
Research Community ◽  
Assessment Tools ◽  
Journal Articles ◽  
Journal Editors ◽  
The Past ◽  
Grant Proposals

This book provides the qualitative research community with some insight on how to evaluate the quality of qualitative research. This topic has gained little attention during the past few decades. We, qualitative researchers, read journal articles, serve on masters’ and doctoral committees, and also make decisions on whether conference proposals, manuscripts, or large-scale grant proposals should be accepted or rejected. It is assumed that various perspectives or criteria, depending on various paradigms, theories, or fields of discipline, have been used in assessing the quality of qualitative research. Nonetheless, until now, no textbook has been specifically devoted to exploring theories, practices, and reflections associated with the evaluation of qualitative research. This book constructs a typology of evaluating qualitative research, examines actual information from websites and qualitative journal editors, and reflects on some challenges that are currently encountered by the qualitative research community. Many different kinds of journals’ review guidelines and available assessment tools are collected and analyzed. Consequently, core criteria that stand out among these evaluation tools are presented. Readers are invited to join the author to confidently proclaim: “Fortunately, there are commonly agreed, bold standards for evaluating the goodness of qualitative research in the academic research community. These standards are a part of what is generally called ‘scientific research.’ ”

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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