Rubinger Luc, Gazendam Aaron, Ekhtiari Seper, Bhandari Mohit
Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada.
Division of Orthopaedics, Department of Surgery, McMaster University, Hamilton, ON Canada; Centre for Evidence-Based Orthopaedics, 293 Wellington St. N, Suite 110, Hamilton, ON L8L 8E7 Canada.
Injury. 2023 May;54 Suppl 3:S69-S73. doi: 10.1016/j.injury.2022.01.046. Epub 2022 Feb 1.
Artificial intelligence (AI) is a broad term referring to the application of computational algorithms that can analyze large data sets to classify, predict, or gain useful conclusions. Under the umbrella of AI is machine learning (ML). ML is the process of building or learning statistical models using previously observed real world data to predict outcomes, or categorize observations based on 'training' provided by humans. These predictions are then applied to future data, all the while folding in the new data into its perpetually improving and calibrated statistical model. The future of AI and ML in healthcare research is exciting and expansive. AI and ML are becoming cornerstones in the medical and healthcare-research domains and are integral in our continued processing and capitalization of robust patient EMR data. Considerations for the use and application of ML in healthcare settings include assessing the quality of data inputs and decision-making that serve as the foundations of the ML model, ensuring the end-product is interpretable, transparent, and ethical concerns are considered throughout the development process. The current and future applications of ML include improving the quality and quantity of data collected from EMRs to improve registry data, utilizing these robust datasets to improve and standardized research protocols and outcomes, clinical decision-making applications, natural language processing and improving the fundamentals of value-based care, to name only a few.
人工智能（AI）是一个广义术语，指的是应用能够分析大型数据集以进行分类、预测或得出有用结论的计算算法。机器学习（ML）属于人工智能范畴。机器学习是利用先前观察到的现实世界数据构建或学习统计模型以预测结果，或根据人类提供的“训练”对观察结果进行分类的过程。然后将这些预测应用于未来数据，同时将新数据融入其不断改进和校准的统计模型中。人工智能和机器学习在医疗保健研究中的未来令人兴奋且前景广阔。人工智能和机器学习正成为医学和医疗保健研究领域的基石，并且在我们持续处理和利用强大的患者电子病历（EMR）数据方面不可或缺。在医疗环境中使用和应用机器学习时需要考虑的因素包括评估作为机器学习模型基础的数据输入质量和决策，确保最终产品是可解释的、透明的，并且在整个开发过程中都要考虑伦理问题。机器学习当前和未来的应用包括提高从电子病历中收集的数据的质量和数量以改善登记数据，利用这些强大的数据集改进和标准化研究方案及结果、临床决策应用、自然语言处理以及改善基于价值的医疗保健的基础等等。
