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Review
. 2023 Jan 7;3(2):189-200.
doi: 10.1016/j.xrrt.2022.12.006. eCollection 2023 May.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Puneet Gupta  1   2 , Heather S Haeberle  2 , Zachary R Zimmer  1 , William N Levine  2 , Riley J Williams  3 , Prem N Ramkumar  3   4
Affiliations
Review

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review

Puneet Gupta et al. JSES Rev Rep Tech. .

Abstract

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature.

Methods: A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both).

Results: A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation.

Conclusion: Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.

Keywords: Artificial intelligence; Machine learning; Orthopedics; Shoulder; Shoulder surgery; Technology.

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Figures

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Figure 1
PRISMA diagram
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