Viderman Dmitriy, Abdildin Yerkin G, Batkuldinova Kamila, Badenes Rafael, Bilotta Federico
Department of Surgery, Nazarbayev University School of Medicine (NUSOM), Kerei, Zhanibek khandar Str. 5/1, Astana 010000, Kazakhstan.
Department of Mechanical and Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, 53 Kabanbay Batyr Ave., Astana 010000, Kazakhstan.
J Clin Med. 2023 Mar 14;12(6):2254. doi: 10.3390/jcm12062254.
Cardiac arrest is a significant cause of premature mortality and severe disability. Despite the death rate steadily decreasing over the previous decade, only 22% of survivors achieve good clinical status and only 25% of patients survive until their discharge from the hospital. The objective of this scoping review was to review relevant AI modalities and the main potential applications of AI in resuscitation.
We conducted the literature search for related studies in PubMed, EMBASE, and Google Scholar. We included peer-reviewed publications and articles in the press, pooling and characterizing the data by their model types, goals, and benefits.
After identifying 268 original studies, we chose 59 original studies (reporting 1,817,419 patients) to include in the qualitative synthesis. AI-based methods appear to be superior to traditional methods in achieving high-level performance.
AI might be useful in predicting cardiac arrest, heart rhythm disorders, and post-cardiac arrest outcomes, as well as in the delivery of drone-delivered defibrillators and notification of dispatchers. AI-powered technologies could be valuable assistants to continuously track patient conditions. Healthcare professionals should assist in the research and development of AI-powered technologies as well as their implementation into clinical practice.
心脏骤停是过早死亡和严重残疾的一个重要原因。尽管在过去十年中死亡率稳步下降，但只有22%的幸存者获得良好的临床状态，只有25%的患者存活至出院。本综述的目的是回顾相关的人工智能模式以及人工智能在复苏中的主要潜在应用。
我们在PubMed、EMBASE和谷歌学术上搜索相关研究。我们纳入了同行评审的出版物和新闻文章，根据其模型类型、目标和益处对数据进行汇总和特征描述。
在确定了268项原始研究后，我们选择了59项原始研究（涉及1,817,419名患者）纳入定性综合分析。基于人工智能的方法在实现高水平性能方面似乎优于传统方法。
人工智能可能有助于预测心脏骤停、心律失常和心脏骤停后的结果，以及在无人机递送除颤器和通知调度员方面发挥作用。人工智能驱动的技术可能是持续跟踪患者病情的有价值助手。医疗保健专业人员应协助人工智能驱动技术的研发及其在临床实践中的应用。
