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AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?

Authors
 YiRang Shin 1, Sungjun Kim 1, Young Han Lee 2 
Citation
 SKELETAL RADIOLOGY, Vol.51(2) : 293-304, 2022-02 
Journal Title
SKELETAL RADIOLOGY
ISSN
 0364-2348 
Issue Date
2022-02
MeSH
Algorithms ; Artificial Intelligence ; Humans ; Magnetic Resonance Imaging ; Musculoskeletal System* / diagnostic imaging ; Radiology*
Keywords
Artificial intelligence ; Deep learning ; Machine learning ; Musculoskeletal system
Abstract
Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing tasks that would otherwise require human intelligence, also at a much faster rate than human performance. In recent years, milestone deep learning models with unprecedented low error rates and high computational efficiency have shown remarkable performance for lesion detection, classification, and segmentation tasks. However, the growing field of AI has significant implications for radiology that are not limited to visual tasks. These are essential applications for optimizing imaging workflows and improving noninterpretive tasks. This article offers an overview of the recent literature on AI, focusing on the musculoskeletal imaging chain, including initial patient scheduling, optimized protocoling, magnetic resonance imaging reconstruction, image enhancement, medical image-to-image translation, and AI-aided image interpretation. The substantial developments of advanced algorithms, the emergence of massive quantities of medical data, and the interest of researchers and clinicians reveal the potential for the growing applications of AI to augment the day-to-day efficiency of musculoskeletal radiologists.
Full Text
https://link.springer.com/article/10.1007/s00256-021-03876-8
DOI
10.1007/s00256-021-03876-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Sungjun(김성준) ORCID logo https://orcid.org/0000-0002-7876-7901
Lee, Young Han(이영한) ORCID logo https://orcid.org/0000-0002-5602-391X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187967
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