Farrow Luke, Meek Dominic, Leontidis Georgios, Campbell Marion, Harrison Ewen, Anderson Lesley
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
Grampian Orthopaedics, Aberdeen Royal Infirmary, Aberdeen, UK.
Bone Joint Res. 2024 Sep 18;13(9):507-512. doi: 10.1302/2046-3758.139.BJR-2024-0135.R1.
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 (https://www.ideal-collaboration.net/). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams.
尽管有大量针对创伤和骨科应用的人工智能（AI）算法已发表，但很少有算法能推进到为临床实践提供信息的阶段。造成这种情况的一个关键原因是缺乏从开发到部署的清晰路径。为了协助这一过程，我们开发了人工智能临床实践整合（CPI-AI）框架——一种基于IDEAL原则（https://www.ideal-collaboration.net/）的五阶段方法，用于在创伤和骨科领域将AI应用于临床实践。遵循该框架将为建立对AI应用的信任提供一个强有力的循证机制，因为临床团队不太可能完全理解其底层算法。
