Understanding Artificial Intelligence and Predictive Analytics

Abstract

Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis

“…Both technologies are increasingly being integrated in the clinical setting, but also in teaching, such as the visualization of organs. In clinical applications, augmented reality enables the simulation of patient encounters to train communication skills or intraoperative decision-making to increase safety during surgery [ 32 ].…”

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

“…Finally, the model should be tested, and its performance (such as the accuracy of fracture detection, dice coefficient, and classification or rate of successful prediction of postoperative outcomes) should be compared with that of both novices and experts in the clinical field. A full description of model performance metrics and interpretation is out of the scope of this article, but an essential component of model development and resources for understanding this are available 31,157,158 . After continual training and evaluation 11 , external validation of the model should be pursued.…”

“…Regarding ML tasks within orthopaedic surgery and its subspecialties, ML has been used to predict clinically significant outcomes in patients undergoing spine procedures, TKA, total hip arthroplasty (THA), total shoulder arthroplasty (TSA), and sports medicine surgery 27 . Further AI-based applications include predicting treatment outcomes in both operative and nonoperative patients, predicting perioperative complications, image feature recognition such as classification of fracture type, and surgery and operating room (OR) optimization 4,[28][29][30][31] . With the vast possibilities for AI utilization in health care, it is important to remember that the ultimate goal of AI and its applications should be aimed at personalized and patient-specific medical care.…”

Evolution of Hybrid Intelligence and Its Application in Evidence-Based Medicine: A Review

“…Even if all things that surround us have become more and more digitalized, and technology has been deeply integrated into modern life, physicians find themselves locked in a pre-Internet era [ 36 ]. Physicians need to be taught the basics to understand AI and the outputs that are given them, in order to integrate them into their decision-making process [ 11 , 37 , 38 ].…”