scholarly journals Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

2019 ◽  
Vol 35 (1) ◽  
pp. 28-35 ◽  
Author(s):  
Joseph E Burns ◽  
Jianhua Yao ◽  
Ronald M Summers
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Musculoskeletal Imaging

scholarly journals AI MSK clinical applications: spine imaging

2021 ◽  
Author(s):  
Florian A. Huber ◽  
Roman Guggenberger
Keyword(s):  
Artificial Intelligence ◽  
Clinical Application ◽  
Value Chain ◽  
Clinical Applications ◽  
Status Quo ◽  
Use Cases ◽  
Musculoskeletal Imaging ◽  
Future Perspective ◽  
Spine Imaging ◽  
The Status

AbstractRecent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 38-49 ◽  
Author(s):  
Natalia Gorelik ◽  
Jaron Chong ◽  
Dana J. Lin
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Bone Tumors ◽  
Image Interpretation ◽  
Bone Age ◽  
Musculoskeletal Imaging ◽  
Radiology Workflow ◽  
Disease Response ◽  
Osseous Metastases ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

scholarly journals Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

2021 ◽  
Vol 16 (05) ◽  
pp. 73
Author(s):  
Jian Huang ◽  
Gang Shen ◽  
Xiping Ren
Keyword(s):  
Artificial Intelligence ◽  
Paradigm Shift ◽  
Learning Ability ◽  
Teaching Activities ◽  
Learning Efficiency ◽  
Teaching Objectives ◽  
Teaching Design ◽  
Teachers And Students ◽  
Artificial Intelligence Technology ◽  
Teaching Efficiency

The influence of artificial intelligence technology on teaching design is explored to improve teaching efficiency. First, artificial intelligence is introduced and its impacts on teaching design are analyzed. Second, the connotation of the paradigm of teaching design and the paradigm shift for teaching design are explored using the paradigm shift analysis framework. Finally, the changes in teaching design under artificial intelligence are analyzed, and the impacts of artificial intelligence on teaching activities are investigated. The results show that the application of artificial intelligence technology has led to different levels of change in the six elements of teaching design, including teaching objectives, service objects (teachers and students), teaching content, teaching media, teaching environment, and teaching evaluation. The connotation and paradigm shift of the teaching design are introduced from the four elements based on the artificial intelligence technology. It is found that artificial intelligence technology can enhance the learning ability and cognitive ability of students to a certain extent while improving the teaching efficiency and learning efficiency. The investigation proves that the teaching design based on artificial intelligence technology can be applied to teaching activities, thereby improving the learning efficiency of students and the teaching efficiency of teachers.

scholarly journals Escaping the McNamara Fallacy: Towards more Impactful Recommender Systems Research

AI Magazine ◽  
2020 ◽  
Vol 41 (4) ◽  
pp. 79-95
Author(s):  
Dietmar Jannach ◽  
Christine Bauer
Keyword(s):  
Artificial Intelligence ◽  
Recommender Systems ◽  
Real World ◽  
Paradigm Shift ◽  
Prediction Accuracy ◽  
Research Community ◽  
Research Culture ◽  
Research Gaps ◽  
Systems Research ◽  
Research Questions

Recommender systems are among today’s most successful application areas of artificial intelligence. However, in the recommender systems research community, we have fallen prey to a McNamara fallacy to a worrying extent: In the majority of our research efforts, we rely almost exclusively on computational measures such as prediction accuracy, which are easier to make than applying other evaluation methods. However, it remains unclear whether small improvements in terms of such computational measures matter greatly and whether they lead us to better systems in practice. A paradigm shift in terms of our research culture and goals is therefore needed. We can no longer focus exclusively on abstract computational measures but must direct our attention to research questions that are more relevant and have more impact in the real world. In this work, we review the various ways of how recommender systems may create value; how they, positively or negatively, impact consumers, businesses, and the society; and how we can measure the resulting effects. Through our analyses, we identify a number of research gaps and propose ways of broadening and improving our methodology in a way that leads us to more impactful research in our field.

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