This systematic review aimed to evaluate the biomechanical properties, functional performance, and clinical outcomes of different hip prosthesis materials and designs used in total hip arthroplasty (THA). A comprehensive search strategy identified 34 peer-reviewed studies published between 2015 and 2024. The materials investigated included cobalt-chromium-molybdenum (CoCrMo), titanium alloys, PEEK, ceramics, and advanced surface coatings such as polycrystalline diamond (PCD). In addition, dual mobility systems, lattice structures, and additively manufactured and patient-specific implants were assessed. The studies utilized clinical trials, finite element analysis, and biomechanical testing to compare outcomes such as wear resistance, stress distribution, osseointegration, and range of motion. The findings demonstrated that titanium alloys and porous lattice structures reduce stress shielding, while ceramics and CoCrMo provide superior wear resistance. Dual mobility implants improved joint stability and range of motion, particularly in high-risk patients. PEEK and PCD showed promising properties but lacked robust long-term data. The integration of advanced manufacturing technologies and material innovations has led to more personalized and biomechanically efficient solutions for THA. Further longitudinal studies are needed to validate these developments. This review provides a critical synthesis of the biomechanical, functional, and clinical implications of contemporary hip prosthetic systems.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

Predictive modeling, supported by machine learning technology, aims to analyze data in order to guide decision-making towards actions generating desired values in the future. It encompasses the set of techniques used to build models that estimate the value of a certain variable predicting a forthcoming event from the past or current values of relevant attributes. In predictive healthcare modeling, the built models represent the relationship among the data concerning customer, provider, production, and other aspects of the healthcare environment in order to assist the decision processes in the prevention of diseases and in the planning of preventive actions by detection of high-risk patients. Contrary to trend analysis, whose goal is to describe past events, predictive models aim to provide useful indications regarding future events and changes. Predictive healthcare modeling supports actions that try to prevent the manifestation of diseases in healthy individuals or try to diagnose as early as possible the incidence of a disease in patients at risk. A sound predictive analysis encompasses not only the model-training task, but also the aspects of data quality, preprocessing, and fusion during its entire implementation lifecycle to ensure appropriate input data preparation. The robustness of the predictive model and its results depends highly on data quality. Due to the variety of data sources in healthcare environments, it becomes essential to use preprocessing in order to remove noise and inconsistencies. The increasing number of endorsable data exchange standards makes each data exchange achievable, but it demands the implementation of a data-governance program. In addition, the influence of the hospital-database architect on the architecture of an early-diagnosis model is important to guarantee appropriate input-formatting modularity.