scholarly journals Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence

2021 ◽  
Vol 16 ◽  
Author(s):  
Karthik Seetharam ◽  
Sirish Shrestha ◽  
Partho P Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Interventional Cardiology ◽  
Cardiovascular Imaging ◽  
Human Intelligence ◽  
Intravascular Imaging ◽  
Additional Information

Artificial Intelligence (AI) is the simulation of human intelligence in machines so they can perform various actions and execute decision-making. Machine learning (ML), a branch of AI, can analyse information from data and discover novel patterns. AI and ML are rapidly gaining prominence in healthcare as data become increasingly complex. These algorithms can enhance the role of cardiovascular imaging by automating many tasks or calculations, find new patterns or phenotypes in data and provide alternative diagnoses. In interventional cardiology, AI can assist in intraprocedural guidance, intravascular imaging and provide additional information to the operator. AI is slowly expanding its boundaries into interventional cardiology and can fundamentally alter the field. In this review, the authors discuss how AI can enhance the role of cardiovascular imaging and imaging in interventional cardiology.

scholarly journals Outlier Detection in High Dimensional Data

2021 ◽  
Vol 10 (5) ◽  
pp. 128-130
Author(s):  
Anusha L. ◽  
Nagaraja G S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Outlier Detection ◽  
Text Processing ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Human Intelligence ◽  
Over Time

Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

scholarly journals Artificial Intelligence in Cardiac Imaging

2020 ◽  
Vol 13 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Karthik Seetharam ◽  
Sirish Shrestha ◽  
Partho P Sengupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Cardiac Imaging ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Precision Phenotyping ◽  
Cardiac Disorders ◽  
Enhance Patient Care

Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

“Fair Notice” in the Age of AI

2020 ◽  
pp. 753-768
Author(s):  
Kiel Brennan-Marquez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Rule Of Law ◽  
Legal System ◽  
Tripartite Model ◽  
Input Output ◽  
The Rule Of Law ◽  
Key Concepts

This chapter examines the concept of “fair notice,” both in the abstract and as it operates in U.S. constitutional doctrine. Fair notice is paramount to the rule of law. The maxim has ancient roots: people ought to know, in advance, what the law demands of them. As such, fair notice will be among the key concepts for regulating the scope and role of artificial intelligence (AI) in the legal system. AI—like its junior sibling, machine learning—unleashes a historically novel possibility: decision-making tools that are at once powerfully accurate and inscrutable to their human stewards and subjects. To determine when the use of AI-based (or AI-assisted) decision-making tools are consistent with the requirements of fair notice, a sharper account of the principle’s contours is needed. The chapter then develops a tripartite model of fair notice, inspired by the problems and opportunities of AI. It argues that lack of fair notice is used interchangeably to describe three distinct properties: notice of inputs, notice of outputs, and notice of input-output functionality. Disentangling these forms of notice, and deciding which matter in which contexts, will be crucial to the proper governance of AI.

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.

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.

scholarly journals Machine Learning in Healthcare Communication

Encyclopedia ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 220-239
Author(s):  
Sarkar Siddique ◽  
James C. L. Chow
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Therapy ◽  
Medical Imaging ◽  
Health Education ◽  
Human Intelligence ◽  
Computer Algorithms ◽  
Dialogue Management ◽  
Topical Review ◽  
Healthcare Communication

Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

Machine Learning and Artificial Intelligence for Surgical Decision Making

2021 ◽  
Vol 22 (6) ◽  
pp. 626-634
Author(s):  
Saskya Byerly ◽  
Lydia R. Maurer ◽  
Alejandro Mantero ◽  
Leon Naar ◽  
Gary An ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making
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