Role of artificial intelligence and point of care ultrasound in management of critically ill patients

Table 1 Demonstrating the different artificial intelligence integrated point of care ultrasound models employed in intensive care units

Ref.	Software utilized	Parameter studied	Result
Yang et al[20], 2024	Intelligent lung ultrasound	Diagnosis of pneumothorax in critically ill patients using intelligent lung ultrasound vs CXR	AI-ultrasound: Sensitivity of 79.4%, specificity of 85.4%. CXR: Sensitivity of 82.4%, specificity of 80.5%
Kessler et al[21], 2024	AI algorithm for detecting features of pulmonary consolidation via POCUS	Identification and localization of lung consolidation using previously confirmed positive and negative images	Overall accuracy of AI algorithm: 88.5%, sensitivity of 88%, specificity of 89%, positive predictive value of 89%, NPV of 87%
Gallant et al[25], 2025	ExoAI LVEF analysis package	LVEF measured by novices with AI assistance vs expert sonographers	Inter-rater reliability between experts and novices was very high (intraclass correlated coefficient of 0.88-0.94)
Dave et al[22], 2023	Previously trained LUS DL model	Comparing real time DL classification to expert classification of normal (A-line pattern) and abnormal (B-line pattern) lung patterns	Real time DL model demonstrated 95% accuracy, 93% sensitivity, and 96% specificity for identification of the B-line pattern
Mento et al[23], 2021	AI-assisted ultrasound DL model	Stratification of patients to low risk or high risk groups for disease progression	High agreement (85.96%) between lung ultrasound interpretation by experts (gold standard), and DL model
Nhat et al[27], 2023	Bespoke AI tool for LUS interpretation	Accuracy of non-clinical examiners before and after AI assistance	Performance improved from 68.9% (95%CI: 65.6%-73.9%) to 82.9% (95%CI: 79.1%-86.7%) (P < 0.001) after use of AI tool
Motazedian et al[35], 2023	FoCUS with AI	FoCUS vs transthoracic echocardiography measurement of LVEF	AUC of 0.98 for abnormal LVEF (< 50%), NPV of 0.97; AUC of 0.99 for severe dysfunction (< 30%)
Zamzmi et al[38], 2023	Multi-stage AI system that utilizes lightweight and open-world machine learning architecture	Automatically computed vs expert calculation of right atrial pressure from inferior vena cava measurement	Excellent agreement (P < 0.01) between groups. Macro accuracy = 0.85
Yildiz Potter et al[40], 2023	YoloV3 object detection algorithm	Pericardial effusion detection in scans confirmed by expert’s vs negative controls	Algorithm had 92% specificity and 89% sensitivity in pericardial effusion identification
Baloescu et al[47], 2020	DL algorithm based on deep convolutional neural networks	Identification of B-lines	DL model had a sensitivity of 93% (95%CI: 81%-98%) for identifying B-lines. High level of agreement with expert interpretations, achieving a kappa of 0.88 (95%CI: 0.79-0.97)
Ienghong et al[48], 2025	Auto B-lines	Detection of pulmonary edema	AI usage resulted in 12.9% increase in sensitivity (95%CI: 0.92-0.98) and a 14% improvement in specificity (95%CI: 0.74-0.80) for diagnosing pulmonary edema compared to manual readings
Leo et al[51], 2023	YoloV3 object detection algorithm	Identification of the presence and location of hemoperitoneum on POCUS by algorithm vs expert	Algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC compared to gold standard expert diagnosis
Cheng et al[52], 2021	CNN based ResNet50-V2	Free-fluid detection in Focused Assessment with Sonography in Trauma examinations	AI model correctly classified positive and negative images with an accuracy of 0.941
Nhat et al[53], 2024	AI tool measuring rectus femoris cross-sectional area	Time spent on scanning	Scanning time reduced from 19.6 minutes (IQR: 16.9-21.7) to 9.4 minutes (IQR: 7.2-11.7) when using AI tool (P < 0.001)
Filipiak-Strzecka et al[55], 2021	LVivo	LVEF	No significant difference between handheld ultrasound and LVivo AI software measurements between (mean difference: -0.61%, 95%CI: -1.89 to 0.68, P = 0.31)
Kuo et al[56], 2022	Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used	Fracture detection: AI model vs clinician	Pooled sensitivity: 92% (95%CI: 88%-93%) for AI and 91% (95%CI: 85%-95%) for clinicians. Pooled specificity: 91% (95%CI: 88%-93%) for AI and 92% (95%CI: 89%-92%) for clinicians. No significant difference found between clinician and AI performance
Holste et al[63], 2025	PanEcho	Successful detection of a variety of cardiac pathology	Successful detection of the following: LVEF (AUC: 0.98-0.99; mean absolute error: 4.2%-4.5%), right ventricular systolic dysfunction (AUC: 0.93-0.94), and severe aortic stenosis (AUC: 0.98-1.00)
Khader et al[66], 2023	Combined medical transformer for multimodal survival prediction	Intensive care unit Survival	CXR model: (AUROC = 0.811), clinical data model (AUROC = 0.785), combined model (AUROC = 0.863, P < 0.001)
Lin et al[67], 2024	PrismICU	Accurate prediction of 30-day mortality	Multimodal approach (AUC = 0.95, F1 score = 0.95) was more accurate than CXR ((AUC = 0.71, F1 score = 0.36) and clinical parameters (AUC = 0.72, F1 score = 0.50) individually

AI: Artificial intelligence; AUC: Area under the curve; AUROC: Area under the receiver operating characteristic; CXR: Chest radiography; DL: Deep learning; FoCUS: Focused cardiac ultrasound; IQR: Interquartile range; LUS: Lung ultrasound; LVEF: Left ventricular ejection fraction; NPV: Negative predictive value; POCUS: Point of care ultrasound.