Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium.
Can J Cardiol. 2022 Feb;38(2):169-184. doi: 10.1016/j.cjca.2021.11.009. Epub 2021 Nov 24.
The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implementation is important for clinicians to fully appreciate the possibilities of AI. Such a foundation would ensure that clinicians have a good grasp and realistic expectations for AI in medicine and prevent discrepancies between the promised and real-world impact. When quantifying the track record for AI applications in cardiology, we found that a substantial number of AI systems are never deployed in clinical practice, although there certainly are many success stories. Successful implementations shared the following: they came from clinical areas where large amount of training data was available; were deployable into a single diagnostic modality; prediction models generally had high performance in external validation; and most were developed as part of collaborations with medical device manufacturers who had substantial experience with implementation of new clinical technology. When looking into the current processes used for developing AI-based systems, we suggest that expanding the analytic framework to address potential deployment and implementation issues at project outset will improve the rate of successful implementation, and will be a necessary next step for AI to achieve its full potential in cardiovascular medicine.
人工智能（AI）革命正在进行中，包括在医学领域，它已经极大地改变了我们的生活。了解 AI 应用的基础知识、它们的开发以及在临床实施方面的挑战，对于临床医生充分了解 AI 的可能性非常重要。这样的基础将确保临床医生对医学中的 AI 有很好的掌握和现实的期望，并防止承诺和现实世界影响之间的差距。当我们量化 AI 在心脏病学中的应用记录时，我们发现大量的 AI 系统从未在临床实践中部署，尽管确实有很多成功的案例。成功的实施案例有以下共同特点：它们来自于有大量训练数据的临床领域；可部署到单一的诊断模式；预测模型在外部验证中通常具有较高的性能；并且大多数都是作为与医疗器械制造商合作的一部分开发的，这些制造商在实施新的临床技术方面拥有丰富的经验。当我们研究当前用于开发基于 AI 的系统的流程时，我们建议在项目开始时扩展分析框架以解决潜在的部署和实施问题，这将提高成功实施的速度，并且是 AI 在心血管医学中充分发挥其潜力的必要下一步。
