Editorial Commentary: Personalized Hip Arthroscopy Outcome Prediction Using Machine Learning—The Future Is Here

Research output: Contribution to journalEditorialpeer-review

Abstract

Machine learning and artificial intelligence are increasingly used in modern health care, including arthroscopic and related surgery. Multiple high-quality, Level I evidence, randomized, controlled investigations have recently shown the ability of hip arthroscopy to successfully treat femoroacetabular impingement syndrome and labral tears. Contemporary hip preservation practice strives to continually refine and improve the value of care provision. Multiple single-center and multicenter prospective registries continue to grow as part of both United States–based and international hip preservation–specific networks and collaborations. The ability to predict postoperative patient-reported outcomes preoperatively holds great promise with machine learning. Machine learning requires massive amounts of data, which can easily be generated from electronic medical records and both patient- and clinician-generated questionnaires. On top of text-based data, imaging (e.g., plain radiographs, computed tomography, and magnetic resonance imaging) can be rapidly interpreted and used in both clinical practice and research. Formidable computational power is also required, using different advanced statistical methods and algorithms to generate models with the ability to predict individual patient outcomes. Efficient integration of machine learning into hip arthroscopy practice can reduce physicians’ “busywork” of data collection and analysis. This can only improve the value of the patient experience, because surgeons have more time for shared decision making, with empathy, compassion, and humanity counterintuitively returning to medicine.

Original languageEnglish (US)
Pages (from-to)1498-1502
Number of pages5
JournalArthroscopy - Journal of Arthroscopic and Related Surgery
Volume37
Issue number5
DOIs
StatePublished - May 2021

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine

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