Use of Artificial Intelligence and Machine Learning in Critical Care Ultrasound.
Authors
Affiliations (2)
Affiliations (2)
- Department of Anaesthesia and Intensive Care Medicine, Hampshire Hospitals NHS Foundation Trust, Winchester, UK. Electronic address: [email protected].
- Faculty of Medicine and Health Science, University of Nottingham, UK. Electronic address: https://twitter.com/cardiacACCP.
Abstract
This article explores the transformative potential of artificial intelligence (AI) in critical care ultrasound AI technologies, notably deep learning and convolutional neural networks, now assisting in image acquisition, interpretation, and quality assessment, streamlining workflow and reducing operator variability. By automating routine tasks, AI enhances diagnostic accuracy and bridges training gaps, potentially democratizing advanced ultrasound techniques. Furthermore, AI's integration into tele-ultrasound systems shows promise in extending expert-level diagnostics to underserved areas, significantly broadening access to quality care. The article highlights the ongoing need for explainable AI systems to gain clinician trust and facilitate broader adoption.