The emergence of artificial intelligence in cardiology: current and future applications

2021 ◽  
Vol 17 ◽  
Author(s):  
Prashanth Kulkarni ◽  
Manjappa Mahadevappa ◽  
Srikar Chilakamarri
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Patient Care ◽  
Current Evidence ◽  
Cognitive Computing ◽  
Cardiovascular Medicine ◽  
Artificial Intelligence Technology ◽  
Application Prospects

: Artificial intelligence technology is emerging as a promising entity in cardiovascular medicine, potentially improving diagnosis and patient care. In this article, we review the literature on artificial intelligence and its utility in cardiology. We provide a detailed description of concepts of artificial intelligence tools like machine learning, deep learning, and cognitive computing. This review discusses the current evidence, application, prospects, and limitations of artificial intelligence in cardiology.

Artificial Intelligence and the Temporality of Machine Images

2021 ◽  
Author(s):  
Andrew R. Johnston
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Video Games ◽  
Learning Strategies ◽  
Learning Algorithms ◽  
Computational Time ◽  
Artificial Intelligence Technology ◽  
Learning Test ◽  
The Relationship

DeepMind, a recent artificial intelligence technology created at Google, references in its name the relationship in AI between models of cognition used in this technology‘s development and its new deep learning algorithms. This chapter shows how AI researchers have been attempting to reproduce applied learning strategies in humans but have difficulty accessing and visualizing the computational actions of their algorithms. Google created an interface for engaging with computational temporalities through the production of visual animations based on DeepMind machine-learning test runs of Atari 2600 video games. These machine play animations bear the traces of not only DeepMind‘s operations, but also of contemporary shifts in how computational time is accessed and understood.

scholarly journals Artificial Intelligence, Machine Learning, and Cardiovascular Disease

2020 ◽  
Vol 14 ◽  
pp. 117954682092740
Author(s):  
Pankaj Mathur ◽  
Shweta Srivastava ◽  
Xiaowei Xu ◽  
Jawahar L Mehta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Prescription Drugs ◽  
Drug Targets ◽  
Data Privacy ◽  
Clinical Medicine ◽  
Treatment Strategies ◽  
Learning Networks ◽  
Cardiovascular Medicine

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

scholarly journals The assessment of Computer Vision Algorithms for the Diagnosis of Prostatic Adenocarcinoma in Surgical Specimens

2020 ◽  
Author(s):  
Syed Usama Khalid Bukhari ◽  
Ubeer Mehtab ◽  
Syed Shahzad Hussain ◽  
Asmara Syed ◽  
Syed Umar Armaghan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Patient Care ◽  
Prostatic Carcinoma ◽  
Digital Images ◽  
Malignant Tumors ◽  
Prostatic Adenocarcinoma ◽  
Histological Diagnosis

Introduction: Prostatic malignancy is a major cause of morbidity and fatality among men around the globe. More than a million new cases of prostatic cancer are diagnoses annually. The incidence of prostatic malignancy is rising and it is expected that more than two million new cases of prostatic carcinoma will be diagnosed in 2040. The application of machine learning to assist the histopathologists could be a very valuable adjunct tool for the histological diagnosis of prostatic malignant tumors. Aim & Objectives: To evaluate the effectiveness of artificial intelligence for the histopathological diagnosis of prostatic carcinoma by analyzing the digitized pathology slides. Materials & Methods: Eight hundred and two (802) images in total, were obtained from the anonymised slides stained with hematoxylin and eosin which included anonymised 337 images of prostatic adenocarcinoma and 465 anonymised images of nodular hyperplasia of prostate. Eighty percent (80%) of the total digital images were used for training and 20% for testing. Three ResNet architectures ResNet-18, ResNet-34, and ResNet-50 were employed for the analysis of these images. Results: The evaluation of digital images by ResNet-18, ResNet-34, and ResNet-50 revealed the diagnostic accuracy of 97.1%, 98 % and 99.5 % respectively. Discussion: The application of artificial intelligence is being considered as a very useful tool which may improve the patient care by improving the diagnostic accuracy and reducing the cost. In radiology, the application of deep learning to interpret radiological images has revealed excellent results. In the present study, the analysis of pathology images by convolutional neural network architecture revealed the diagnostic accuracy of 97.1%, 98 % and 99.5 % with by ResNet-18, ResNet-34, and ResNet-50 respectively. The findings of the present study are in accordance with the other published series, which were carried out to determine the accuracy of machine learning for the diagnosis of cancers of lung, breast and prostate. The application of deep learning for the histological diagnosis of malignant tumors could be quite helpful in improving the patient care. Conclusion: The findings of the present study suggest that intelligent vision system possibly a worthwhile tool for the histopathological evaluation of prostatic tissue to differentiate between the benign and malignant disorders.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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.

Development of migrant workers in construction based on machine learning and artificial intelligence technology

2020 ◽  
pp. 1-12
Author(s):  
Chen Guang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Labor Force ◽  
Migrant Workers ◽  
Engineering Industry ◽  
Agricultural Industry ◽  
Construction Engineering ◽  
Artificial Intelligence Technology ◽  
Chinese Labor ◽  
The Times

Artificial intelligence technology has been widely used in all aspects of our life. Similarly, the application of artificial intelligence in the field of construction engineering is a necessary trend in the development of engineering industry, especially in the traditional construction engineering department. Under the background of the times, from the perspective of knowledge, artificial intelligence technology has appeared a huge development, which may have an impact on the employment of Chinese labor force, may create new jobs, or replace traditional jobs. This effect on employment is essential. From the perspective of machine learning and artificial intelligence, this paper reviews the transformation prospects of engineering industry and the development of agricultural industry in construction industry, and examines the intellectual transformation of individual human capital in Chinese labor force.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Towards Neuromorphic Learning Machines Using Emerging Memory Devices with Brain-Like Energy Efficiency

2018 ◽  
Vol 8 (4) ◽  
pp. 34 ◽  
Author(s):  
Vishal Saxena ◽  
Xinyu Wu ◽  
Ira Srivastava ◽  
Kehan Zhu
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Memory Devices ◽  
Cognitive Computing ◽  
Mixed Signal ◽  
Von Neumann ◽  
Emerging Memory

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven by the increasing amount of pattern classification and cognitive tasks. Specialized digital hardware for deep learning still holds its predominance due to the flexibility offered by the software implementation and maturity of algorithms. However, it is being increasingly desired that cognitive computing occurs at the edge, i.e., on hand-held devices that are energy constrained, which is energy prohibitive when employing digital von Neumann architectures. Recent explorations in digital neuromorphic hardware have shown promise, but offer low neurosynaptic density needed for scaling to applications such as intelligent cognitive assistants (ICA). Large-scale integration of nanoscale emerging memory devices with Complementary Metal Oxide Semiconductor (CMOS) mixed-signal integrated circuits can herald a new generation of Neuromorphic computers that will transcend the von Neumann bottleneck for cognitive computing tasks. Such hybrid Neuromorphic System-on-a-chip (NeuSoC) architectures promise machine learning capability at chip-scale form factor, and several orders of magnitude improvement in energy efficiency. Practical demonstration of such architectures has been limited as performance of emerging memory devices falls short of the expected behavior from the idealized memristor-based analog synapses, or weights, and novel machine learning algorithms are needed to take advantage of the device behavior. In this article, we review the challenges involved and present a pathway to realize large-scale mixed-signal NeuSoCs, from device arrays and circuits to spike-based deep learning algorithms with ‘brain-like’ energy-efficiency.

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