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Review
. 2025 May 28:15563316251339660.
doi: 10.1177/15563316251339660. Online ahead of print.

Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient

Miguel M Girod  1 , Sami Saniei  1 , Marisa N Ulrich  1   2 , Lainey G Bukowiec  1   2 , Kellen L Mulford  1 , Michael J Taunton  1   2 , Cody C Wyles  1   2
Affiliations
Review

Artificial Intelligence in the Diagnosis and Prognostication of the Musculoskeletal Patient

Miguel M Girod et al. HSS J. .

Abstract

As artificial intelligence (AI) advances in healthcare, encompassing robust applications for the diagnosis and prognostication of musculoskeletal diseases, clinicians must increasingly understand the implications of machine learning and deep learning in their practice. This review article explores computer vision algorithms and patient-specific, multimodal prediction models; provides a simple framework to guide discussion on the limitations of AI model development; and introduces the field of generative AI.

Keywords: artificial intelligence; computer vision; deep learning; diagnosis and prognosis; generative AI; machine learning; musculoskeletal health; orthopedic surgery.

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Conflict of interest statement

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Michael J. Taunton reports relationships with Depuy, DJO Global, Stryker, Journal of Arthoplasty, AAHKS, and AAOS. Cody C. Wyles reports relationships with DePuy and the AAHKS Research Committee. The other authors declare no potential conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Conceptual difference between machine learning and traditional statistics.
Fig. 2.
Fig. 2.
Example of input (blue), hidden (yellow), and output (red) layers in an artificial neural network. The metric quantifies the difference between the model’s prediction and the ground truth and informs the loss function. The loss function then controls the process of backpropagation. Backpropagation results in an update of the model’s weights, which leads to more accurate predictions. The cycle is repeated until the metric is optimized (ie, when the difference between predictions and ground truth labels is closest to zero) and the loss function is at its minimal value.
See this image and copyright information in PMC

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