The First Hospital of China Medical University, Shenyang, China.
Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China.
J Med Internet Res. 2024 Jul 26;26:e47645. doi: 10.2196/47645.
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.
近年来，人工智能（AI）呈爆炸式发展，已广泛应用于医疗保健领域。机器学习模型作为一种典型的 AI 技术，通过利用大量医疗数据进行训练和优化，在预测心血管疾病方面展现出巨大潜力，有望在降低心血管疾病发病率和死亡率方面发挥关键作用。尽管该领域已成为研究热点，但仍存在许多陷阱，研究人员需密切关注。这些陷阱可能会影响研究模型的预测性能、可信度、可靠性和可重复性，最终降低研究价值并影响临床应用前景。因此，在实施研究之前，识别和避免这些陷阱是一项至关重要的任务。然而，目前针对这一主题缺乏全面的总结。本观点旨在分析数据质量、数据集特征、模型设计和统计方法以及临床意义方面存在的问题，并提供可能的解决方案，例如收集客观数据、改进训练、重复测量、增加样本量、使用统计方法防止过拟合、使用特定的 AI 算法解决特定问题、标准化结果和评估标准、增强公平性和可重复性，以期为研究人员、算法开发人员、政策制定者和临床从业者提供参考和帮助。
