Muthukumar A, Schreck A, Guerra-Londono CE, Tabbara AK, Uribe-Marquez S. Role of artificial intelligence and point of care ultrasound in management of critically ill patients. World J Crit Care Med 2026; 15(2): 114201 [DOI: 10.5492/wjccm.v15.i2.114201]
Corresponding Author of This Article
Arun Muthukumar, MD, Department of Anesthesiology, Henry Ford Hospital, 2799 W Grand Blvd, Detroit, MI 48202, United States. amarimu1@hfhs.org
Research Domain of This Article
Anesthesiology
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Minireviews
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Muthukumar A, Schreck A, Guerra-Londono CE, Tabbara AK, Uribe-Marquez S. Role of artificial intelligence and point of care ultrasound in management of critically ill patients. World J Crit Care Med 2026; 15(2): 114201 [DOI: 10.5492/wjccm.v15.i2.114201]
World J Crit Care Med. Jun 9, 2026; 15(2): 114201 Published online Jun 9, 2026. doi: 10.5492/wjccm.v15.i2.114201
Role of artificial intelligence and point of care ultrasound in management of critically ill patients
Arun Muthukumar, Alexander Schreck, Carlos E Guerra-Londono, Abdul Kader Tabbara, Santiago Uribe-Marquez
Arun Muthukumar, Carlos E Guerra-Londono, Abdul Kader Tabbara, Santiago Uribe-Marquez, Department of Anesthesiology, Henry Ford Hospital, Detroit, MI 48202, United States
Alexander Schreck, College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, United States
Author contributions: Muthukumar A was responsible for manuscript original draft, editing, resources, and conceptualization; Schreck A was responsible for manuscript original draft, editing, and resources; Guerra-Londono CE was responsible for manuscript original draft and conceptualization; Tabbara AK was responsible for manuscript original draft and editing; Uribe-Marquez S was responsible for manuscript editing and conceptualization; all of the authors read and approved the final version of the manuscript to be published.
AI contribution statement: No AI tools including ChatGPT, Grammarly and DeepL were used in the preparation of this manuscript.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Corresponding author: Arun Muthukumar, MD, Department of Anesthesiology, Henry Ford Hospital, 2799 W Grand Blvd, Detroit, MI 48202, United States. amarimu1@hfhs.org
Received: September 15, 2025 Revised: November 10, 2025 Accepted: January 19, 2026 Published online: June 9, 2026 Processing time: 250 Days and 4.1 Hours
Abstract
Point of care ultrasound (POCUS) has become an invaluable tool in the management of critically ill patients, offering real-time diagnostic insights into cardiovascular, pulmonary, and abdominal pathophysiology. In the critical care setting, timely diagnosis is essential to manage life-threatening conditions. The integration of artificial intelligence (AI) into POCUS has been a transformative technological advancement in the field. AI-incorporated POCUS can help clinicians of varying experience levels overcome limitations associated with operator dependency and varied image quality. These innovations are valuable, not only in resource-constrained settings, but also during time-sensitive clinical scenarios, profoundly impacting patient outcomes. AI-driven platforms can provide prompt feedback of protocolized exams such as rapid ultrasound in shock and bedside lung ultrasound in emergency. This is especially relevant in situations, where basic imaging such as transthoracic echocardiography is often performed by non-specialized personnel. Most AI tools remain investigational, and the need for robust validation of machine learning in clinical workflows remains a burning question. Despite a promising role in simulation, the effectiveness of these tools in real-world clinical scenarios depends heavily on the quality of training datasets. The integration of AI and POCUS, while reducing diagnostic errors, is also revolutionizing diagnostics with deep learning models, which demonstrate high accuracy. Beyond improving diagnostic precision, AI is optimizing workflows, reducing processing times, and enabling real-time interpretation. Future advancements in AI, integrating imaging with clinical data and predictive modeling, have the potential to significantly enhance prognostic accuracy and patient outcomes, particularly in critical care settings. This narrative review aims to explore the current applications, advancements, and future directions of AI-assisted POCUS in the intensive care unit, with a particular focus on machine learning in critically ill patients.
Core Tip: By synthesizing the latest evidence, our article provides critical care practitioners and researchers with a comprehensive overview of how artificial intelligence-enhanced point of care ultrasound can transform bedside decision-making, improve patient outcomes, and expand access to high-quality care worldwide.
