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

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Abstract

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.

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Authors and Affiliations

  1. The Center of Artificial Intelligence in Medical Imaging, Division of Musculoskeletal Imaging, The University of California At Irvine, 101 The City Dr S, Orange, CA, 92868, USA

    Simukayi Mutasa

  2. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Intelligent Imaging Center, University of Maryland School of Medicine, 22 South Greene Street, First Floor, Baltimore, MD, 21201, USA

    Paul H. Yi

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  1. Simukayi Mutasa
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  2. Paul H. Yi
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Correspondence to Simukayi Mutasa.

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Mutasa, S., Yi, P.H. Deciphering musculoskeletal artificial intelligence for clinical applications: how do I get started?. Skeletal Radiol 51, 271–278 (2022). https://doi.org/10.1007/s00256-021-03850-4

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  • DOI: https://doi.org/10.1007/s00256-021-03850-4

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