Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles ( Cite this article:
High failure rates of metal-on-metal hip arthroplasty implants have highlighted the need for more careful introduction and monitoring of new implants and for the evaluation of the safety of medical devices. The National Joint Registry and other regulatory services are unable to detect failing implants at an early enough stage. We aimed to identify validated surrogate markers of long-term outcome in patients undergoing primary total hip arthroplasty (THA). We conducted a systematic review of studies evaluating surrogate markers for predicting long-term outcome in primary THA. Long-term outcome was defined as revision rate of an implant at ten years according to National Institute of Health and Care Excellence guidelines. We conducted a search of Medline and Embase (OVID) databases. Separate search strategies were devised for the Cochrane database and Google Scholar. Each search was performed to include articles from the date of their inception to June 8, 2015.Objectives
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