Potential benefits, unintended consequences, and future roles of artificial intelligence in orthopaedic surgery research: a call to emphasize data quality and indications

Abstract

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.

Keywords

• artificial intelligence
• machine-learning
• data management
• predictive modelling
• orthopedics
• orthopaedic surgery
• orthopaedic surgeon
• clinicians
• total joint replacement
• total knee arthroplasty (tka)
• total hip arthroplasty
• preoperative ct scans
• cognitive function
• arthroplasty
• anterior cruciate ligament (acl) tears