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      Current clinical applications of artificial intelligence in shoulder surgery: what the busy shoulder surgeon needs to know and what’s coming next

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          Abstract

          Background

          Artificial intelligence (AI) is a continuously expanding field with the potential to transform a variety of industries—including health care—by providing automation, efficiency, precision, accuracy, and decision-making support for simple and complex tasks. Basic knowledge of the key features as well as limitations of AI is paramount to understand current developments in this field and to successfully apply them to shoulder surgery. The purpose of the present review is to provide an overview of AI within orthopedics and shoulder surgery exploring current and forthcoming AI applications.

          Methods

          PubMed and Scopus databases were searched to provide a narrative review of the most relevant literature on AI applications in shoulder surgery.

          Results

          Despite the enormous clinical and research potential of AI, orthopedic surgery has been a relatively late adopter of AI technologies. Image evaluation, surgical planning, aiding decision-making, and facilitating patient evaluations over time are some of the current areas of development with enormous opportunities to improve surgical practice, research, and education. Furthermore, the advancement of AI-driven strategies has the potential to create a more efficient medical system that may reduce the overall cost of delivering and implementing quality health care for patients with shoulder pathology.

          Conclusion

          AI is an expanding field with the potential for broad clinical and research applications in orthopedic surgery. Many challenges still need to be addressed to fully leverage the potential of AI to clinical practice and research such as privacy issues, data ownership, and external validation of the proposed models.

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          Most cited references79

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          ImageNet classification with deep convolutional neural networks

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            Dermatologist-level classification of skin cancer with deep neural networks

            Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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              Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

              Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
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                Author and article information

                Contributors
                Journal
                JSES Rev Rep Tech
                JSES Rev Rep Tech
                JSES Reviews, Reports, and Techniques
                Elsevier
                2666-6391
                07 September 2023
                November 2023
                07 September 2023
                : 3
                : 4
                : 447-453
                Affiliations
                [a ]Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
                [b ]Department of Orthopedic Surgery, Pontificia Universidad Católica de Chile, Santiago, Chile
                [c ]Shoulder and Elbow Unit, Hospital Dr. Sótero del Rio, Santiago, Chile
                [d ]Orthopedic Surgery Artificial Intelligence Lab (OSAIL), Mayo Clinic, Rochester, MN, USA
                [e ]Department of Orthopaedic Surgery, George Washington University School of Medicine and Health Sciences, Washington, DC, USA
                [f ]Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA
                Author notes
                []Corresponding author: John W. Sperling, MD, MBA, Department of Orthopedic Surgery, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. sperling.john@ 123456mayo.edu
                Article
                S2666-6391(23)00080-9
                10.1016/j.xrrt.2023.07.008
                10625013
                b1767a13-5bd5-4c00-b512-5ce3eff23590
                © 2023 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Reviews and Full Length Articles

                artificial intelligence,machine learning,deep learning,shoulder surgery,decision-making,computer vision,preoperative planning,shoulder

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