Use of artificial intelligence in critical care: opportunities and obstacles

Abstract

Background: 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.

Main body: 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.

Conclusions: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Keywords: Complexity; Healthcare policy; Machine learning; Predictive analytics; Systems engineering.

Fig. 1

The process of creating, testings, and launching an effective Clinical Decision Support System (CDSS) is multifaceted and ongoing. The interaction of multiple processes and involvement of various stakeholders along the way improve the likelihood of final adoption during real-world deployment (dashed vertical line). Importantly, as illustrated in this work flow diagram, is ongoing assessment refining models and information transfer options. At the start one uses a model card which is a short document that provides key information about a machine learning model. This is central to maintaining focus throughout the workflow cycle of CDSS development