Key Concepts: Algorithms, Artificial Intelligence, and More

2020 ◽  
pp. 13-47
Keyword(s):  
Artificial Intelligence ◽  
Key Concepts

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

scholarly journals Artificial Intelligence in Thyroid Field. A Comprehensive Review

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4740
Author(s):  
Fabiano Bini ◽  
Andrada Pica ◽  
Laura Azzimonti ◽  
Alessandro Giusti ◽  
Lorenzo Ruinelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cognitive Abilities ◽  
Medical Diagnostics ◽  
Critical Discussion ◽  
Research Field ◽  
Response To Treatment ◽  
Key Concepts ◽  
Mathematical Algorithms

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

scholarly journals Towards Human-Engaged AI

2018 ◽  
Author(s):  
Xiaojuan Ma
Keyword(s):  
Artificial Intelligence ◽  
Human Computer Interaction ◽  
Driving Force ◽  
Research Paradigm ◽  
Computing Systems ◽  
Key Concepts ◽  
Computer Interaction

Engagement, the key construct that describes the synergy between human (users) and technology (computing systems), is gaining increasing attention in academia and industry. Human-Engaged AI (HEAI) is an emerging research paradigm that aims to jointly advance the capability and capacity of human and AI technology. In this paper, we first review the key concepts in HEAI and its driving force from the integration of Artificial Intelligence (AI) and Human-Computer Interaction (HCI). Then we present an HEAI framework developed from our own work.

Artificial Intelligence for Decision-Makers

2019 ◽  
Vol 17 (1) ◽  
pp. 51-55 ◽  
Author(s):  
Viktor H. Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools ◽  
Jel Classifications

ABSTRACT We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns. JEL Classifications: A29; C44; C45; D81; M41.

Artificial Intelligence in Education

2019 ◽  
pp. 224-236 ◽  
Author(s):  
Nil Goksel ◽  
Aras Bozkurt
Keyword(s):  
Artificial Intelligence ◽  
Learning Styles ◽  
Language Processing ◽  
Adaptive Learning ◽  
Future Perspectives ◽  
Tutoring Systems ◽  
Educational Processes ◽  
Key Concepts ◽  
Artificial Intelligence In Education ◽  
Educational Perspective

Though only a dream a while ago, artificial intelligence (AI) has become a reality, being now part of our routines and penetrating every aspect of our lives, including education. It is still a field in its infancy, but as time progresses, we will witness how AI evolves and explore its untapped potential. Against this background, this chapter examines current insights and future perspectives of AI in various contexts, such as natural language processing (NLP), machine learning, and deep learning. For this purpose, social network analysis (SNA) is used as a guide for the interpretation of the key concepts in AI research from an educational perspective. The research identified three broad themes: (1) adaptive learning, personalization and learning styles, (2) expert systems and intelligent tutoring systems, and (3) AI as a future component of educational processes.

The Application of AI Teachers in Facilitating Game-Based Literacy Learning

2022 ◽  
pp. 381-395
Author(s):  
Yixun Li ◽  
Lin Zou
Keyword(s):  
Artificial Intelligence ◽  
Scientific Community ◽  
Empirical Studies ◽  
Scientific Research ◽  
Literacy Learning ◽  
Research Area ◽  
Classroom Teaching ◽  
Theoretical Frameworks ◽  
Key Concepts ◽  
Human Teachers

This chapter discusses the theoretical frameworks for artificial intelligence (AI) teachers and how AI teachers have been applied to facilitate game-based literacy learning in existing empirical studies. While the application of artificial intelligence (AI) in education is a relatively emerging research area, it has received increasing attention in the scientific community. In the future, AI teachers are likely to be able to serve as powerful supplementary tools in classroom teaching in support of human teachers. The main goal here is to provide the readers with new insights on promoting game-based literacy learning from the perspectives of AI teachers. To this end, the authors introduce the readers to the key concepts of AI teachers, the merits and demerits of AI teachers in education, scientific research on AI teachers in literacy learning, and some highlighted examples of AI teachers in literacy classrooms for practical concerns.

Artificial Intelligence and Machine Learning in Radiology

2019 ◽  
pp. 199-226
Author(s):  
Michael A. Bruno
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Diagnostic Radiology ◽  
Job Description ◽  
Error Prevention ◽  
Human Endeavor ◽  
Key Concepts ◽  
The Impact

This final chapter, which assumes no prior reader knowledge of the topic, reviews the promise of artificial intelligence (AI), especially machine learning and deep learning in radiology. We initially discuss key concepts in the field of AI and gain a broad overview of the field and its potential, as well as the impact it is having on multiple areas of human endeavor. Subsequently, we focus on current and projected aspects of AI as applied to diagnostic radiology, specifically on how AI might provide an avenue for error prevention and remediation in radiology. The possible impact of AI in changing the radiologist’s role and basic job description is also considered.

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