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      A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

      review-article
      1 , 2 , 3 ,
      Arthroplasty
      BioMed Central
      Artificial intelligence (AI), Deep learning, Knee arthroplasty, Knee replacement, Machine learning

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          Abstract

          Background

          Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value.

          Methods

          Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps.

          Results

          A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as “demonstration of concepts” rather than “proof of concepts”. There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited.

          Conclusion

          While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

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          Most cited references76

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          Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?

          This article was presented at the 2017 annual meeting of the American Association of Hip and Knee Surgeons to introduce the members gathered as the audience to the concepts behind artificial intelligence (AI) and the applications that AI can have in the world of health care today. We discuss the origin of AI, progress to machine learning, and then discuss how the limits of machine learning lead data scientists to develop artificial neural networks and deep learning algorithms through biomimicry. We will place all these technologies in the context of practical clinical examples and show how AI can act as a tool to support and amplify human cognitive functions for physicians delivering care to increasingly complex patients. The aim of this article is to provide the reader with a basic understanding of the fundamentals of AI. Its purpose is to demystify this technology for practicing surgeons so they can better understand how and where to apply it.
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            Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

            With the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care. Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.
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              Robotics in Arthroplasty: A Comprehensive Review

              Robotic-assisted orthopedic surgery has been available clinically in some form for over 2 decades, claiming to improve total joint arthroplasty by enhancing the surgeon's ability to reproduce alignment and therefore better restore normal kinematics. Various current systems include a robotic arm, robotic-guided cutting jigs, and robotic milling systems with a diversity of different navigation strategies using active, semiactive, or passive control systems. Semiactive systems have become dominant, providing a haptic window through which the surgeon is able to consistently prepare an arthroplasty based on preoperative planning. A review of previous designs and clinical studies demonstrate that these robotic systems decrease variability and increase precision, primarily focusing on component positioning and alignment. Some early clinical results indicate decreased revision rates and improved patient satisfaction with robotic-assisted arthroplasty. The future design objectives include precise planning and even further improved consistent intraoperative execution. Despite this cautious optimism, many still wonder whether robotics will ultimately increase cost and operative time without objectively improving outcomes. Over the long term, every industry that has seen robotic technology be introduced, ultimately has shown an increase in production capacity, improved accuracy and precision, and lower cost. A new generation of robotic systems is now being introduced into the arthroplasty arena, and early results with unicompartmental knee arthroplasty and total hip arthroplasty have demonstrated improved accuracy of placement, improved satisfaction, and reduced complications. Further studies are needed to confirm the cost effectiveness of these technologies.
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                Author and article information

                Contributors
                andrew.kurmis@sa.gov.au
                Journal
                Arthroplasty
                Arthroplasty
                Arthroplasty
                BioMed Central (London )
                2524-7948
                4 July 2023
                4 July 2023
                2023
                : 5
                : 40
                Affiliations
                [1 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, Discipline of Medical Specialties, , University of Adelaide, ; Adelaide, SA 5005 Australia
                [2 ]GRID grid.460761.2, ISNI 0000 0001 0323 4206, Department of Orthopaedic Surgery, , Lyell McEwin Hospital, ; Haydown Road, Elizabeth Vale, SA 5112 Australia
                [3 ]GRID grid.1014.4, ISNI 0000 0004 0367 2697, College of Medicine & Public Health, , Flinders University, ; Bedford Park, SA 5042 Australia
                Author information
                http://orcid.org/0000-0002-9951-1374
                Article
                189
                10.1186/s42836-023-00189-0
                10318748
                37400876
                2938a7a4-ae66-4b6c-82e9-8f860eb360b2
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2023
                : 19 April 2023
                Categories
                Review
                Custom metadata
                © Arthroplasty Society in Asia 2023

                artificial intelligence (ai),deep learning,knee arthroplasty,knee replacement,machine learning

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