Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?

Artificial intelligence (AI) represents a broad category of algorithms for which deep learning is currently the most impactful. When electing to begin the process of building an adequate fundamental knowledge base allowing them to decipher machine learning research and algorithms, clinical musculosk...

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Publicado en:Skeletal Radiology Vol. 51; no. 2; pp. 271 - 279
Autores principales: Mutasa, Simukayi, Yi, Paul H.
Formato: review Journal Article
Publicado: Springer Nature Feb2022
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?
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          Mutasa, Simukayi
          Yi, Paul H.
        affil: The Center of Artificial Intelligence in Medical Imaging, Division of Musculoskeletal Imaging, The University of California At Irvine, 101 The City Dr S, 92868, Orange, CA, USA
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          Specialties, Medical
          Artificial Intelligence
          Algorithms
          Clinical Assessment Tools
          Scales
      ab: Artificial intelligence (AI) represents a broad category of algorithms for which deep learning is currently the most impactful. When electing to begin the process of building an adequate fundamental knowledge base allowing them to decipher machine learning research and algorithms, clinical musculoskeletal radiologists currently have few options to turn to. In this article, we provide an introduction to the vital terminology to understand, how to make sense of data splits and regularization, an introduction to the statistical analyses used in AI research, a primer on what deep learning can or cannot do, and a brief overview of clinical integration methods. Our goal is to improve the readers' understanding of this field.
      pubtype: Academic Journal
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        Journal Article
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    language: English
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