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Artificial intelligence evaluation of patient physical examination and MRI data using a machine learning model shows that arthroscopically confirmed partial\u2010 or full\u2010thickness subscapularis tears are highly associated with abnormal subscapularis tendon length, long head of the biceps tears, and subscapularis fatty atrophy, and on physical examination, with weakness with internal rotation and positive lift\u2010off, belly press, and bear hug tests. Today, physicians may use machine learning as a tool, but this model may not currently be sufficient to drastically change practice. However, with continued research and development, which is occurring rapidly, similar models could aid physicians in timely identification of pathology and optimization of preoperative planning, as well as physician training and education.<\/jats:p>","DOI":"10.1016\/j.arthro.2023.10.012","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T15:47:32Z","timestamp":1704988052000},"page":"1056-1058","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["<i>Editorial Commentary:<\/i>\n                    Artificial Intelligence Models Using Machine Learning Can Improve Preoperative Identification of Subscapularis Pathology"],"prefix":"10.1002","volume":"40","author":[{"given":"James A.","family":"Pruneski","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery Tripler Army Medical Center Honolulu Hawaii 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