Rohan M Shah1, Clarissa Wong2, Nicholas C Arpey2, Alpesh A Patel2, Srikanth N Divi3. 1. Northwestern University, Evanston, IL, USA. 2. Department of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. 3. Department of Orthopaedic Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. srikanth.divi@nm.org.
Abstract
PURPOSE OF REVIEW: In recent years, machine learning techniques have been increasingly utilized across medicine, impacting the practice and delivery of healthcare. The data-driven nature of orthopaedic surgery presents many targets for improvement through the use of artificial intelligence, which is reflected in the increasing number of publications in the medical literature. However, the unique methodologies utilized in AI studies can present a barrier to its widespread acceptance and use in orthopaedics. The purpose of our review is to provide a tool that can be used by practitioners to better understand and ultimately leverage AI studies. RECENT FINDINGS: The increasing interest in machine learning across medicine is reflected in a greater utilization of AI in recent medical literature. The process of designing machine learning studies includes study design, model choice, data collection/handling, model development, training, testing, and interpretation. Recent studies leveraging ML in orthopaedics provide useful examples for future research endeavors. This manuscript intends to create a guide discussing the use of machine learning and artificial intelligence in orthopaedic surgery research. Our review outlines the process of creating a machine learning algorithm and discusses the different model types, utilizing examples from recent orthopaedic literature to illustrate the techniques involved.
PURPOSE OF REVIEW: In recent years, machine learning techniques have been increasingly utilized across medicine, impacting the practice and delivery of healthcare. The data-driven nature of orthopaedic surgery presents many targets for improvement through the use of artificial intelligence, which is reflected in the increasing number of publications in the medical literature. However, the unique methodologies utilized in AI studies can present a barrier to its widespread acceptance and use in orthopaedics. The purpose of our review is to provide a tool that can be used by practitioners to better understand and ultimately leverage AI studies. RECENT FINDINGS: The increasing interest in machine learning across medicine is reflected in a greater utilization of AI in recent medical literature. The process of designing machine learning studies includes study design, model choice, data collection/handling, model development, training, testing, and interpretation. Recent studies leveraging ML in orthopaedics provide useful examples for future research endeavors. This manuscript intends to create a guide discussing the use of machine learning and artificial intelligence in orthopaedic surgery research. Our review outlines the process of creating a machine learning algorithm and discusses the different model types, utilizing examples from recent orthopaedic literature to illustrate the techniques involved.
Authors: Vladimir Svetnik; Andy Liaw; Christopher Tong; J Christopher Culberson; Robert P Sheridan; Bradley P Feuston Journal: J Chem Inf Comput Sci Date: 2003 Nov-Dec
Authors: J Matthew Helm; Andrew M Swiergosz; Heather S Haeberle; Jaret M Karnuta; Jonathan L Schaffer; Viktor E Krebs; Andrew I Spitzer; Prem N Ramkumar Journal: Curr Rev Musculoskelet Med Date: 2020-02
Authors: Thomas G Myers; Prem N Ramkumar; Benjamin F Ricciardi; Kenneth L Urish; Jens Kipper; Constantinos Ketonis Journal: J Bone Joint Surg Am Date: 2020-05-06 Impact factor: 5.284