Artificial Intelligence is Pushing The Boundaries in Orthopaedic Surgery
Keywords:
Artificial IntelligenceAbstract
Three convergences of the digital world have an impact on world life, the use of artificial intelligence (AI), robotics, and autonomy. This can be seen clearly in the development of many industries in the world, whose valuation increases are companies that are able to adopt these three things. It seems to have grown quite exponentially and heavily influenced medicine. It reveals that nearly 72% of AI research within orthopedics have been published in the past 2 years.1 Artificial intelligence (AI) is the application of algorithms that provide machines the ability to solve problems that traditionally required human intelligence. AI at its core involves machines that can perform tasks innately characteristic of human intelligence. This includes tasks like planning, understanding language, recognizing patterns, learning, and problem-solving. AI can be thought of as an umbrella term that encompasses a broad range of subfields, including machine learning (ML), which in turn contains a subfield called deep learning (DL).2,3
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References
Murphy MP, Brown NM. CORR synthesis: when should
the orthopaedic surgeon use artificial intelligence,
machine learning, and deep learning? Clin Orthop Relat
Res. 2021; 479(7):1497-505.
Scarlat A. A machine learning primer for clinicians–part
HIStalk. 2018. Ac- cessed 2019 Aug 5. https://histalk2.
com/2018/10/17/a-machine-learning-primer-forclinicians-part-1.
Chollet F. Deep learning with Python. Shelter Island,
New York: Manning Publications; 2018.
Mohri M, Rostamizadeh A, Talwalkar A. Foundations of
Machine Learning. 2nd ed. Cham, Switzerland: MIT
Press; 2018.
Thong W, Parent S, Wu J, Aubin CE, Labelle H, Kadoury
S. Three-dimensional morphology study of surgical
adolescent idiopathic scoliosis patient from encoded
geometric models. Eur Spine J. 2016;25(10):3104-13.
Shah RF, Martinez AM, Pedoia V, Majumdar S, Vail TP,
Bini SA. Variation in the thickness of knee cartilage. The
use of a novel machine learning algorithm for cartilage
segmentation of magnetic resonance images. J Arthroplasty. 2019 Oct; 34(10):2210-5.
Chung SW, Han SS, Lee JW, Oh KS, Kim NR, Yoon JP,
Kim JY, Moon SH, Kwon J, Lee HJ, Noh YM, Kim Y.
Automated detection and classification of the proximal
humerus fracture by using deep learning algorithm. Acta
Orthop. 2018;89(4):468-73.
Ramkumar PN, Karnuta JM, Navarro SM, Haeberle HS,
Iorio R, Mont MA, Patterson BM, Krebs VE. Preoperative
prediction of value metrics and a patient-specific
payment model for primary total hip arthroplasty:
development and validation of a deep learning model. J
Arthroplasty. 2019 Oct;34(10):2228-2234.e1. Epub 2019 M.
Print. Robotic orthopedic surgery - overview. Mayo
Clinic. Available at: https://www.mayoclinic.org/
departments-centers/robotic-orthopedic-surgery/
overview/ovc- 20472153. Accessed March 9, 2022.
Eibold M, Maurer S, Hoch A, Zingg P, Farshad M, Navab
N, Furnstahl P. Real-time acoustic sensing and artificial
intelligence for error prevention in orthopedic surgery.
Sci Rep. 2021;11(1):3993
