2023
DOI: 10.12998/wjcc.v11.i18.4231
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Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Maryam Salimi,
Joshua A Parry,
Raha Shahrokhi
et al.

Abstract: The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previou… Show more

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Cited by 4 publications
(3 citation statements)
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“…Deep learning is a type of ML that uses an artificial neural network to process an input through multilayered, interconnected nodes ('deep')[ 1 ]. We read an article by Salimi et al [ 2 ] with interest and found that AI has many roles in trauma orthopedics. We would like to highlight the importance of AI in rehabilitation medicine especially for motor recovery.…”
Section: To the Editormentioning
confidence: 99%
“…Deep learning is a type of ML that uses an artificial neural network to process an input through multilayered, interconnected nodes ('deep')[ 1 ]. We read an article by Salimi et al [ 2 ] with interest and found that AI has many roles in trauma orthopedics. We would like to highlight the importance of AI in rehabilitation medicine especially for motor recovery.…”
Section: To the Editormentioning
confidence: 99%
“…These filters are learned during the training process, allowing the network to identify patterns such as edges, textures, and shapes at different scales [ 26 , 27 ]. CNNs also typically include other types of layers, such as pooling layers, which downsample the feature maps to reduce computational complexity, and fully connected layers [ 28 ].…”
Section: Introductionmentioning
confidence: 99%
“…Although the potential benefits of AI technology in the field of orthopedics are substantial, there are still several challenges and considerations that should be evaluated and addressed, such as data quality, interoperability, regulatory compliance, ethical considerations, and the need for interdisciplinary collaboration. To date, numerous studies have been conducted to review AI applications in some subspecialties of orthopedics [ 2 , 6 , 14 , 16 , 28 , 30 ]. However, there has not yet been a comprehensive review of the recent advancements in shoulder pathology.…”
Section: Introductionmentioning
confidence: 99%