Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent “black-box” nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

中文翻译：

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人工智能在重症监护中的应用：机遇与障碍

也许在医疗保健系统中，没有其他地方比重症监护病房环境更能创建具有直接时间紧迫的临床应用的有用模型的挑战，并且实现这些目标的障碍更大。基于机器学习的人工智能（AI）技术来定义状态和预测未来事件是现代生活的常见活动。然而，它们在急症护理医学中的渗透却缓慢、断断续续且参差不齐。广泛有效应用人工智能方法对危重病人进行实时护理的主要障碍是存在的，需要解决。急症和重症监护环境中的临床决策支持系统 (CDSS) 为临床医生提供支持，而不是在床边取代他们。正如本次综述中将讨论的，原因有很多，包括基于人工智能的系统在态势感知方面的不成熟，许多大型数据库中存在根本偏差，这些数据库不能反映正在接受治疗的患者的目标人群，这使得公平性成为一个重要问题解决及时获取有效数据及其以对临床工作流程有用的方式显示的技术障碍。许多预测算法和 CDSS 固有的“黑匣子”性质使得医学界难以信任和接受。从逻辑上讲，对各种来源的实时多维数据流进行整理和整理，以通知算法并最终显示适应个体患者反应和签名的相关临床决策支持格式，代表这些系统的传出肢，在初始验证期间经常被忽略努力。同样，访问许多现有临床数据库的法律和商业障碍限制了解决预测模型和管理工具的公平性和普遍性的研究。基于人工智能的 CDSS 正在不断发展，并将持续存在。我们有义务成为它们的使用和进一步发展的好牧人。