en عمومى General Narrative Review <p></p> <div style="text-align: justify;"><span style="font-size:14px;"><span style="font-family:Times New Roman;">Total hip arthroplasty (THA) is one of the most successful orthopaedic operations. However, the increased volume of these treatments is accompanied by an increased number of post-operative problems and revision rates. Traditional statistical models for risk stratification sometimes fail to account for complicated non-linear interactions among patient-specific variables. Thus, machine learning (ML), a subset of artificial intelligence, has been proposed as a viable method in the orthopedic literature to optimize predictive accuracy. The purpose of this narrative review was to critically appraise the current research on the application of machine learning approaches to predict short-term problems, long-term implant survival, and revision rates after THA. A literature search of relevant publications on the development and clinical validation of ML algorithms was performed in PubMed and Scopus. A review of the literature available shows that ML models, and specifically ensemble methods such as Random Forest and Gradient Boosting, have outperformed traditional logistic regression in predicting surgical site infections, dislocations, and readmissions, and have produced robust survival analyses for long-term implant failure. However, currently, these benefits are constrained by the reliance on retrospective data, algorithmic bias and the &lsquo;black-box&rsquo; character of sophisticated models that limit the broad clinical integration of ML. ML has the transformative potential to tailor preoperative counseling, but additional prospective, multicenter studies using comprehensible artificial intelligence (XAI) are required to ensure safe clinical adoption.</span></span></div> Machine learning, Total hip arthroplasty, Complication prediction, Revision arthroplasty, Artificial intelligence. 21 27 http://jrhms.thums.ac.ir/browse.php?a_code=A-10-219-1&slc_lang=en&sid=1 Ali Ghanbarloo 10031947532846002347 10031947532846002347 Yes Department of Operating Room, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran