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

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 singlecenter and multicenter prospective registries continue to grow as part of both United Statesebased and international hip preservationespecific 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.