Total hip arthroplasty (THA) is a frequently performed surgery where even slight improvements in precision and efficiency can lead to significant clinical advantages. This narrative review synthesises current evidence on AI applications along the THA care pathway, spanning preoperative planning, intraoperative execution, outcome prediction, and postoperative monitoring. In preoperative planning, AI-enabled 3-dimensional templating improves implant size prediction compared with conventional 2-dimensional methods and markedly reduces planning time while enhancing component positioning and biomechanical restoration. Intraoperatively, AI-integrated robotic and navigation platforms increase the proportion of components placed within radiographic "safe zones," although short-term functional outcomes often remain comparable to conventional techniques, highlighting the need to link imaging surrogates with patient-centred endpoints. Across outcome prediction tasks - including patient-reported outcomes, complications, revision, and periprosthetic joint infection - machine learning models generally achieve good discriminative performance and consistently underscore the prognostic importance of preoperative symptom burden and comorbidity. Nevertheless, most studies remain at an early translational stage, with limited external validation, few prospective or randomised impact evaluations, incomplete assessment of bias and fairness, and ongoing uncertainty around regulatory, reimbursement, and workflow integration. Future research should prioritise robust multicentre validation, prospective studies that quantify clinical and economic impact, systematic reporting of model performance across patient subgroups, and closer collaboration among clinicians, data scientists, regulators, and industry to support safe and equitable adoption of AI throughout the THA pathway.
Wu et al. (Sun,) studied this question.
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