Open AccessDoes Artificial Intelligence Outperform Natural Intelligence in Interpreting Musculoskeletal Radiological Studies? A Systematic Review
Author(s) -
Olivier Q. Groot,
Michiel E R Bongers,
Paul T. Ogink,
Joeky T. Senders,
Aditya V. Karhade,
Jos A. M. Bramer,
JorritJan Verlaan,
Joseph H. Schwab
Publication year - 2020
Publication title -
clinical orthopaedics and related research
Language(s) - English
Resource type - Journals
eISSN - 1528-1132
pISSN - 0009-921X
DOI - 10.1097/corr.0000000000001360
Subject(s) - medicine , medical physics , systematic review , checklist , medline , critical appraisal , orthopedic surgery , medical diagnosis , artificial intelligence , cochrane library , machine learning , radiological weapon , physical therapy , meta analysis , radiology , pathology , surgery , alternative medicine , computer science , psychology , political science , law , cognitive psychology
Machine learning (ML) is a subdomain of artificial intelligence that enables computers to abstract patterns from data without explicit programming. A myriad of impactful ML applications already exists in orthopaedics ranging from predicting infections after surgery to diagnostic imaging. However, no systematic reviews that we know of have compared, in particular, the performance of ML models with that of clinicians in musculoskeletal imaging to provide an up-to-date summary regarding the extent of applying ML to imaging diagnoses. By doing so, this review delves into where current ML developments stand in aiding orthopaedists in assessing musculoskeletal images.