Role of artificial intelligence and point of care ultrasound in management of critically ill patients.
Authors
Affiliations (3)
Affiliations (3)
- Department of Anesthesiology, Henry Ford Hospital, Detroit, MI 48202, United States. [email protected].
- College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, United States.
- Department of Anesthesiology, Henry Ford Hospital, Detroit, MI 48202, United States.
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.