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Abstract
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.
The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
In the 1960s, total hip replacement revolutionised management of elderly patients crippled with arthritis, with very good long-term results. Today, young patients present for hip-replacement surgery hoping to restore their quality of life, which typically includes physically demanding activities. Advances in bioengineering technology have driven development of hip prostheses. Both cemented and uncemented hips can provide durable fixation. Better materials and design have allowed use of large-bore bearings, which provide an increased range of motion with enhanced stability and very low wear. Minimally invasive surgery limits soft-tissue damage and facilitates accelerated discharge and rehabilitation. Short-term objectives must not compromise long-term performance. Computer-assisted surgery will contribute to reproducible and accurate placement of implants. Universal economic constraints in healthcare services dictate that further developments in total hip replacement will be governed by their cost-effectiveness.
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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