• Michael R Pinsky, Armando Bedoya, Azra Bihorac, Leo Celi, Matthew Churpek, Nicoleta J Economou-Zavlanos, Paul Elbers, Suchi Saria, Vincent Liu, Patrick G Lyons, Benjamin Shickel, Patrick Toral, David Tscholl, and Gilles Clermont.
• Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA. pinsky@pitt.edu.
• Crit Care. 2024 Apr 8; 28 (1): 113113.

BackgroundPerhaps 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 BodyClinical 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.ConclusionsAI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.© 2024. The Author(s).

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