Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases

2017 ◽  
Vol 14 (2) ◽  
pp. 198-207 ◽  
Author(s):  
Silvia Cascianelli ◽  
Michele Scialpi ◽  
Serena Amici ◽  
Nevio Forini ◽  
Matteo Minestrini ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Molecular Imaging ◽  
Neurodegenerative Diseases ◽  
Imaging Modalities ◽  
Artificial Intelligence Techniques

scholarly journals Application of artificial intelligence in pancreaticobiliary diseases

2021 ◽  
Vol 14 ◽  
pp. 263177452199305
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Zhongheng Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Response To Treatment ◽  
Endoscopic Retrograde ◽  
Artificial Intelligence Techniques ◽  
Computer Aided ◽  
Rapid Pace ◽  
Cystic Pancreatic Lesions ◽  
Aided Diagnosis

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

scholarly journals The role of artificial intelligence techniques in scheduling systems

1990 ◽  
Vol 7 (3-4) ◽  
pp. 231-242 ◽  
Author(s):  
Amy L. Geoffroy ◽  
Daniel L. Britt ◽  
John R. Gohring
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Techniques

The Role of Artificial Intelligence Within the Scope of Digital Transformation in Enterprises

2020 ◽  
pp. 122-146 ◽  
Author(s):  
M. Hanefi Calp
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Digital Transformation ◽  
New Era ◽  
Artificial Intelligence Techniques ◽  
Reasoning Problem ◽  
Analytical Thinking ◽  
Effective Service

Digital transformation, which is the beginning of a new era, and performed in order to provide a more effective service, has become a compulsory situation for the enterprises that take into account the increasing corporate volumes. However, the processes and technologies used in this transformation may change according to the enterprise volume and needs. At this point, activities that implement artificial intelligence technologies will make significant contributions to digital transformation. Artificial intelligence technologies serve many purposes such as search, reasoning, problem-solving, perception, learning, estimating, analytical thinking, optimization, and planning. The purpose of this chapter is to demonstrate the effects of artificial intelligence techniques on the processes of digital transformation utilized in enterprises by considering the difficulties experienced in the realization of digital transformation. It is expected that the study will provide a perspective for other studies on digital transformation and thus create an awareness.

scholarly journals Role of Artificial Intelligence in Medicine and Clinical Research

2021 ◽  
Vol 9 (8) ◽  
pp. 1512-1518
Author(s):  
Jhumpa Sarma
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Clinical Settings ◽  
Business Sector ◽  
Process Automation ◽  
Artificial Intelligence Techniques ◽  
Fuzzy Expert Systems ◽  
Artificial Neural ◽  
Time Required

Abstract: Artificial Intelligence is a branch of computer science that enables to analyse complex medical data. The proficiency of artificial intelligence techniques has been explored to a great extent in the field of medicine. Most of the medications go to the business sector after a long tedious process of drug development. It can take a period of 10-15 years or more to convey a medication from its introductory revelation to the hands of the patients. Artificial Intelligence can significantly reduce the time required and can also cut down the expenses by half. Among the methods, artificial neural network is the most widely used analytical tool while other techniques like fuzzy expert systems, natural language processing, robotic process automation and evolutionary computation have been used in different clinical settings. The aim of this paper is to discuss the different artificial intelligence techniques and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on medical education, physicians, healthcare institutions and bioethics. Keywords: Artificial intelligence, clinical trials, medical technologies, artificial neural networks, diagnosis.

scholarly journals The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

2020 ◽  
Vol 7 ◽  
Author(s):  
Karthik Seetharam ◽  
Daniel Brito ◽  
Peter D. Farjo ◽  
Partho P. Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Cardiovascular Imaging ◽  
Review Article ◽  
Imaging Modalities ◽  
Data Explosion ◽  
Digital Landscape

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

Translatability of Law and Legal Technology

2020 ◽  
pp. 520-540
Author(s):  
Wolfgang Alschner ◽  
John Mark Keyes
Keyword(s):  
Artificial Intelligence ◽  
Federal Legislation ◽  
Federal Regulations ◽  
Artificial Intelligence Techniques ◽  
Tax Legislation ◽  
Legal Texts ◽  
Technology Applications ◽  
Legal Technology

Lawyers and citizens increasingly engage with law through technology intermediaries. For example, to declare their taxes, they consult tax software rather than tax legislation. This greater role of legal technology raises new issues for bilingual jurisdictions. In Canada, for instance, federal legislation is not translated but simultaneously codrafted by francophone and anglophone lawyers, resulting in small differences in the expression of the law and occasional inconsistencies. This contribution showcases how these differences can affect legal technology applications. Depending on the language they work with, lawyers may encode different interpretations in software, and algorithms may yield different results. Using a bilingual corpus of 3,000 Canadian federal regulations as a case study, the authors demonstrate that the same artificial intelligence techniques applied to the same legal texts in different languages yield different results. As a consequence, they argue that legal technology cannot simply be developed for one language and then translated to another language. Instead, it has to be “codeveloped” for different languages, similar to how legislation can be “codrafted.”

scholarly journals CHALLENGES OF USING DIFFERENT MATHEMATICAL MODELS FOR LOAD BALANCING OPTIMIZATION IN MULTI-CORE COMPUTING SYSTEMS

2020 ◽  
Vol 3 (2) ◽  
pp. 190-195
Author(s):  
Nigar Ismayilova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Load Balancing ◽  
Mathematical Models ◽  
Dynamic Environment ◽  
Computing Systems ◽  
Artificial Intelligence Techniques

This paper examines the role of applying different artificial intelligence techniques for the implementation of load balancing in the dynamic environment of distributed multi-core computing systems. Were investigated several methods to optimize the assignment process between computing nodes and executing tasks after the occurrence of a dynamic and interactive event, when traditional discrete load balancing techniques are ineffective.

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