Clinical and Research Medical Applications of Artificial Intelligence

• Published in: Arthroscopy Vol. 37; no. 5; pp. 1694 - 1697
• Main Authors: Ramkumar, Prem N., Kunze, Kyle N., Haeberle, Heather S., Karnuta, Jaret M., Luu, Bryan C., Nwachukwu, Benedict U., Williams, Riley J.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2021; by the Arthroscopy Association of North America
• Subjects: Algorithms; Artificial Intelligence; Biomedical Research; Humans; Level V Evidence; Machine Learning; Patient Reported Outcome Measures; Sports Medicine
• ISSN: 0749-8063; 1526-3231
• DOI: 10.1016/j.arthro.2020.08.009

Artificial intelligence (AI), including machine learning (ML), has transformed numerous industries through newfound efficiencies and supportive decision-making. With the exponential growth of computing power and large datasets, AI has transitioned from theory to reality in teaching machines to automate tasks without human supervision. AI-based computational algorithms analyze “training sets” using pattern recognition and learning from inputted data to classify and predict outputs that otherwise could not be effectively analyzed with human processing or standard statistical methods. Though widespread understanding of the fundamental principles and adoption of applications have yet to be achieved, recent applications and research efforts implementing AI have demonstrated great promise in predicting future injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting telehealth. With appreciation, caution, and experience applying AI, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. The purpose of this review is to discuss the pearls, pitfalls, and applications associated with AI.