Artificial Intelligence and Echocardiography: From Literacy to Leadership, Redefining Roles, Responsibilities, and Sustainability in the Artificial Intelligence-driven Echocardiography Era

Abstract

Artificial intelligence (AI) is rapidly reshaping echocardiography, evolving from experimental applications to regulated clinical tools that automate acquisition, quantification, and outcome prediction. Yet for many practitioners, AI terminology remains elusive and disconnected from routine care. This review provides an educational and conceptual framework to bridge technical innovation with clinical reasoning. We outline the foundations of AI literacy, covering machine learning, deep learning, dataset construction, and validation, while appraising the literature through contemporary methodological standards for cardiovascular imaging AI. We then describe four progressive levels of clinical value, from automation and standardization to disease phenotyping, prognostic modeling, and sustainability. Particular emphasis is placed on explainable AI, governance models, AI-ready laboratories, and the need for external validation and postdeployment accountability. Beyond efficiency, AI has ethical and societal implications: Cloud-based platforms and hub-and-spoke models may democratize access to high-quality echocardiography, supporting more equitable global care. Finally, we discuss evolving professional roles, educational frameworks, and the integration of generative and multimodal AI. The overarching goal is to guide clinicians from basic literacy to informed leadership in an era where echocardiography is increasingly AI-integrated rather than merely AI assisted.

INTRODUCTION

At nearly every scientific meeting dedicated to artificial intelligence (AI) in echocardiography, the conceptual framework is introduced through a familiar hierarchy: AI as the simulation of human cognitive functions, machine learning (ML) as pattern recognition from data, and deep learning (DL) as neural network-based modeling. Although technically accurate, these definitions often remain elusive for cardiac imaging practitioners and offer limited guidance on how AI should be interpreted, evaluated, and integrated into routine clinical care.

While several comprehensive publications[1,2,3,4,5,6,7,8] have described the expanding applications of AI in echocardiography, a gap persists between technical innovation and clinical understanding. This review is intended for clinicians with limited familiarity with AI methodologies and aims to bridge that gap by providing a structured, clinically grounded framework [Graphical Abstract]. Key terminology is summarized in Table 1. The literature was critically appraised in alignment with contemporary recommendations for cardiovascular imaging AI evaluation, with attention to study design, data provenance, reference standards, validation strategies, clinical utility, interpretability, and generalizability, as formalized in the PRIME 2.0 checklist.[9]

Graphical Abstract.

AI literacy as the foundation of clinical leadership. Educational, technical, and governance competencies converge toward precision medicine and sustainable implementation

Table 1.

Glossary of artificial intelligence terminology for echocardiography: Clinically oriented definitions to support basic artificial intelligence literacy

Term		Clinically oriented definition
Algorithm		A step-by-step procedure (the recipe). By itself, it does not learn; it learns only when trained on data
Program		Executable code that implements an algorithm on a computer (the recipe in action)
Model		The trained artifact produced after learning from data embeds parameters that can make predictions for new cases
AI		Umbrella term for computer systems performing tasks that typically require human-like capabilities (perception, classification, language)
ML		A subset of AI where models learn patterns from data to make predictions (classification/regression)
DL		ML using deep neural networks that learn directly from raw images/video without handcrafted features
Supervised learning/training		Learning with labeled inputs and labeled outputs (e.g., echo features → diagnosis). Ground truth guides the model
Unsupervised learning/training		Learning from unlabeled data to discover structure (e.g., clustering phenotypes from strain/volume patterns)
Semi-supervised learning		Training with a small labeled set plus many unlabeled examples to improve performance when labels are scarce
Self-supervised learning		Model learns from pretext tasks on unlabeled data (e.g., predicting missing frames) and is later fine-tuned on labels
Reinforcement learning		The model learns by trial and error to maximize a reward signal (rare in echo, potential for probe guidance/automation)
Transfer learning		Reusing knowledge from a pre-trained model (often on large image datasets) and adapting it to echocardiography
Fine-tuning		Adjusting a pre-trained model’s weights on your specific echo dataset to improve performance/fit
GT		Reference standard used for training/testing (e.g., expert contours or CMR volumes). Model quality depends on its quality
Label (annotation)		The ‘correct answer’ is attached to each training example (e.g., “EF<40%”)
Raw data		Unprocessed inputs (pixels/voxels/cine-loops) are used mainly by DL models
Feature (structured variable)		Many ML models use a measured attribute (e.g., LVEDV, GLS)
CV		AI operating directly on images/video (segmentation, view recognition, motion tracking)
NLP		AI operating on text (e.g., converting narrative echo reports to structured data; drafting reports)
End-to-end versus multi-step		End-to-end: Direct input → output (video → EF). Multi-step: Sequential tasks (view → segmentation → volumes → EF)
Pipeline		The full processing flow (acquisition → preprocessing → model → post-processing → report)
Training/validation/test		Data splits are used for model development; the test set remains untouched until the final evaluation
External validation		Testing on data from other labs/vendors/populations to assess generalizability
Cross-validation		Rotating train/validation splits to use data efficiently and estimate robustness
Overfitting		The model learns the noise/specifics of the training data; it performs poorly on new patients
Underfitting		The model is too simple and fails to capture relevant cardiac patterns
Generalization		Ability to maintain performance on unseen patients across sites/vendors
Domain shift/data drift/concept drift		Changes in imaging protocols, populations, or disease patterns that degrade model performance over time
Calibration		Agreement between predicted risk and observed outcomes is important for setting decision thresholds
Threshold		Cut-off applied to model output to trigger an action (e.g., EF flag). Impacts sensitivity/specificity
Performance metrics		Accuracy, sensitivity, specificity, PPV/NPV, AUC, F1–choose based on class balance and clinical use
Class imbalance		Unequal distribution of diagnoses (e.g., a small number of amyloidosis cases) can bias training and metrics
Data augmentation		Creating realistic variations (noise, rotation) to improve robustness, especially in CV
Loss function		The mathematical measure of the model’s error during training; optimization aims to minimize this function so that predictions approach the ground truth
Optimization/backpropagation		Process through which a model improves during training by measuring prediction error (loss) and iteratively adjusting its parameters to minimize it, gradually learning the configuration that best reproduces correct outputs
Image-to-image translation		Converting images between modalities/domains (e.g., echo → CMR-like) to harmonize measurements
Generative AI		Models that create synthetic data (GANs, diffusion) for augmentation, translation, anomaly detection, or text generation
XAI		Techniques to make model decisions interpretable (saliency maps, SHAP/LIME, concept-based explanations)
Black box		A model whose internal decision process is opaque; requires validation and, when possible, XAI
Propagation bias		Systematic errors in ground truth/labels are learned and perpetuated by the model
Data leakage/label leakage		Unintended overlap of information between train and test (or inclusion of future info) that inflates performance
Harmonization/vendor-neutral		Methods and platforms to reduce vendor/probe differences; improve multi-site comparability
Interoperability (DICOM, PACS)		Standards and systems that allow AI tools to integrate with acquisition and reporting workflows
Privacy/De-identification		Removal of patient identifiers and secure handling of data to comply with legal and ethical standards

Terms are phrased for day-to-day use in echocardiographic practice. AI=Artificial intelligence, AUC=Area under the curve, CMR=Cardiovascular magnetic resonance, CNN=Convolutional neural network, CV=Computer vision, DICOM=Digital imaging and communications in medicine, DL=Deep learning, EF=Ejection fraction, F1=F1-score, GANs=Generative adversarial networks, GLS=Global longitudinal strain, GT=Ground truth, LIME=Local interpretable model-agnostic explanations, LVEDV=Left ventricular end-diastolic volume, ML=Machine learning, NLP=Natural language processing, NPV=Negative predictive value, PACS=Picture archiving and communication system, PPV=Positive predictive value, SHAP=Shapley additive explanations, XAI=Explainable AI

FOUNDATIONS OF ALGORITHMIC LEARNING

In contrast to conventional ultrasound systems, which execute predefined instructions and yield consistent outputs regardless of variability, AI-based algorithms iteratively update internal parameters through exposure to data [Figure 1]. This adaptive interaction with data enables models to refine performance beyond fixed rule-based processing. With sufficient computational power, it identifies recurring patterns and correlations, gradually building a trained model that can generalize to unseen cases.[10] Intelligence, therefore, is a misnomer from an epistemological standpoint: It is a mathematical process arising from repeated exposure to data and continuous parameter optimization.[11] The algorithm does not “understand” the task; it iteratively minimizes error until predictions become accurate enough to approximate expert reasoning. In this learning process, three elements are fundamental: (i) a loss function that quantifies prediction error, (ii) an optimization algorithm (e.g., gradient descent) that reduces this error, and (iii) iterative exposure to data that progressively refines model accuracy.[12] A crucial part of learning is also teaching the algorithm what not to learn, that is, to ignore random noise and irrelevant associations that do not generalize.

Figure 1.

From ML to DL: how models learn in echocardiography. Traditional programs follow fixed sequential rules (black circles in a straight arrow). ML uses predefined features and simple decision paths (black circles with solid connections). DL adds multilayer neural representations (white circles, dashed links) that learn patterns directly from raw data

MACHINE LEARNING AND DEEP LEARNING PARADIGMS

For clinicians familiar with training new echocardiographers, the trainee metaphor is intuitive. A junior trainee learns from explicit labels and structured feedback (“this ventricle is dilated,” “this is hypertrophic cardiomyopathy”) much like supervised ML, which maps predefined inputs to labeled outputs. By contrast, an advanced trainee develops pattern recognition through broad exposure to complete studies, paralleling DL models that learn directly from raw data (cine loops, volumetric datasets, and pixel intensities) without predefined rules.[13] In supervised ML, expert-labeled and structured variables are used to train predictive models. In unsupervised ML, no labels are provided; instead, the algorithm identifies latent structures within the data. In echocardiography, this may involve clustering patients by strain curves or chamber indices, revealing data-driven phenotypes that can refine disease classification. However, such clusters remain mathematical constructs and require external validation before clinical application.[14]

Supervised and unsupervised approaches are complementary: The former reinforces established categories, while the latter enables discovery beyond predefined diagnostic boundaries.[12]

OVERVIEW OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

There is no single “AI algorithm” for echocardiography, just as no single practitioner masters every aspect of cardiac imaging. Algorithms differ in both design and purpose [Table 2]. Some are developed to classify, predict, cluster, or interpret imaging data, whereas others analyze structured clinical variables or learn directly from raw image pixels and temporal signals.[15] Their selection should be driven by the clinical question, such as chamber segmentation, discrimination between physiological and pathological hypertrophy, phenotyping of remodeling patterns, or prognostic estimation, rather than by the algorithm itself. AI literacy, therefore, requires understanding not only how an algorithm functions, but when and why to apply it.[12]

Table 2.

Overview of machine learning and deep learning algorithms in echocardiography

Algorithm		Definition		Analogy		Relevance in echocardiography
Logistic regression		Interpretable baseline classifier relying on expert-selected variables		A clinician applying a clear diagnostic threshold		Serves as a benchmark for prediction tasks such as identifying LV dysfunction
Decision tree		Hierarchical model dividing data through sequential rules		A clinical flowchart guiding decisions		Useful for rule-based triage and explainable diagnostic pathways
Random forest (bagging)		An ensemble of decision trees reduces variance and overfitting		A panel of clinicians voting on the best decision		Stable predictions from variable echo measurements (EF, GLS)
Boosting (XGBoost, AdaBoost)		Sequential models correcting prior errors for higher accuracy		A trainee improves after reviewing each mistake		High performance in multiparametric prognostic modeling (HFpEF, remodeling)
Stacking		Meta-learner integrating outputs from different models		A multidisciplinary heart team synthesizing opinions		Ideal for combining clinical, Doppler, and imaging information
SVM		A margin-based classifier is effective in high-dimensional spaces		Drawing the most precise boundary between two patient groups		Used for distinguishing HCM from an athlete’s heart
KNN		Similarity-based classifier using the nearest cases		Diagnosing by recalling similar past patients		Useful for phenotype similarity and for grouping remodeling patterns
K-means clustering		Unsupervised grouping into k clusters based on similarity		Organizing patients into natural categories		Applied to LVDD and 3D geometric phenotypes
Hierarchical clustering		Nested clustering does not require a predefined k		A family tree of related phenotypes		Supports echo-based phenotyping of diastolic or atrial patterns
PCA		Dimensionality reduction, extracting major components		Summarizing a long story into key themes		Simplifies correlated echo variables (volumes, strain)
CNN		DL models extracting spatial features from images		A pattern-recognition expert is examining each frame		Automated view classification, LV/LA segmentation, EF/GLS estimation
RNN/LSTM		Models capturing temporal dependencies in sequential data		A clinician tracking trends over time		Analyze Doppler signals, ECG–echo interactions, longitudinal follow-up
Autoencoders		Neural networks learning compressed latent representations		Capturing the hidden essence of a patient profile		Identify latent geometric or functional phenotypes (HFpEF)

EF=Ejection fraction, SVM=Support vector machine, KNN=K-nearest neighbors, PCA=Principal component analysis, HfpEF=Heart failure with preserved EF, ECG=Electrocardiogram, RNN=Recurrent neural network, LSTM=Long short-term memory, LV=Left ventricle, LA=Left atrium, GLS=Global longitudinal strain, CNN=Convolutional neural network, DL=Deep learning, 3D=Three dimensional, LVDD=Left ventricular diastolic dysfunction, HCM=Hypertrophic cardiomyopathy

Traditional ML models (e.g., logistic regression, decision trees, random forests, and support vector machines) rely on expert-selected features. They are well-suited to prediction and clustering using structured clinical and echocardiographic data. DL models form a distinct paradigm: Convolutional neural networks (CNNs) learn directly from images for view classification, segmentation, and EF or GLS quantification, while recurrent architectures (Recurrent neural networks, Long short-term memories) model Doppler signals and temporal trajectories. Representation-learning approaches, such as autoencoders, further enable the identification of latent phenotypes beyond conventional measurements.

No algorithm is inherently superior; the key lies in aligning the appropriate method with the clinical task.[10]

GROUND TRUTH AND THE BALANCE BETWEEN MACHINE LEARNING AND DEEP LEARNING

A model does not learn in isolation. Clinicians and computational data scientists provide the algorithm, the training data, and the ground truth, the correct answers against which predictions are compared. Ultimately, ground truth is the mirror through which AI reflects our own biases: The accuracy of annotations is as critical as the sophistication of the algorithm.[16] In echocardiography, the ground truth typically derives from expert annotation (e.g., manual chamber contouring) or comparison with cardiac magnetic resonance (CMR), the gold standard for volumetric quantification. Model reliability depends on the quality of this reference. Inconsistent labeling or imperfect standards are inherited by the algorithm, a phenomenon known as propagation bias. For instance, if 2D volumes are used as ground truth, their systematic underestimation relative to CMR will persist in model predictions. ML is more interpretable and data-efficient but relies on expert-defined inputs. Compared with traditional ML, DL models operate directly on high-dimensional imaging inputs and autonomously learn the representations needed for a given task. This approach typically yields superior performance for image-intensive applications, such as segmentation or view classification, but relies on large, well-annotated datasets and provides more limited interpretability.

PREPARING THE ARTIFICIAL INTELLIGENCE-READY ECHO LAB: STANDARDS FOR RELIABILITY AND EXPLAINABLE OUTPUTS

Transitioning to an AI-ready echocardiography laboratory is not simply a technological upgrade but a cultural transformation. Integration requires infrastructural readiness, such as advanced imaging capabilities, server-based analysis, and PACS interoperability, as well as organizational adaptation, including silent-mode testing, staff training, and structured validation protocols. Adoption spans a continuum, from limited AI use to near-autonomous workflows operating under formal governance and medico-legal oversight. Infrastructure alone, however, does not ensure trust. Clinicians are unlikely to adopt opaque “black-box” systems. Explainable AI (XAI), therefore, plays a central role in aligning computational outputs with clinical reasoning [Table 3]. Feature-attribution methods (e.g., SHAP, LIME), saliency mapping, concept-based explanations, and surrogate modeling increase transparency, strengthen regulatory accountability, and provide educational insight into how algorithms reach their conclusions.[17]

Table 3.

Explainable artificial intelligence approaches in echocardiography

XAI approach		Application in echocardiography		Benefits		Limitations
Feature attribution (SHAP, LIME)		Quantifies feature contributions in radiomic and clinical-echo models; risk stratification		Individualized explanations; identifies dominant predictors; improves trust		Computationally intensive; explanations vary across methods
Saliency and heat maps		Visualize image regions influencing CNN classification (e.g., view recognition, pathology detection)		Intuitive; highlights relevant structures; educational utility		May be unstable or misleading; limited quantitative insight
Concept-based explanations		Connect predictions to clinical descriptors (LV sphericity, wall thickness, strain)		Aligns AI reasoning with echocardiographic knowledge		Requires predefined concepts; limited generalizability
Model simplification (decision trees, rules)		Surrogate models approximating complex architectures		Transparent, user-friendly; supports bedside use		Accuracy loss; risk of oversimplification

AI=Artificial intelligence, LV=Left ventricle, XAI=Explainable AI, SHAP=Shapley additive explanations, LIME=Local interpretable model-agnostic explanations, CNN=Convolutional neural network

DATASET PARTITIONING IN MACHINE LEARNING/DEEP LEARNING TRAINING

Training an ML or DL model can be conceptualized as a staged learning process. The training set provides labeled examples from which patterns are learned; the validation set refines model parameters and mitigates overfitting; and the test set evaluates performance on previously unseen data. True robustness, however, requires external validation, assessment in independent laboratories and populations, and across different ultrasound vendors to demonstrate generalizability and clinical reliability. Without such external testing, high internal performance may not translate into real-world applicability.

THE (CURRENT) CLINICAL VALUE SCALE OF ARTIFICIAL INTELLIGENCE IN ECHOCARDIOGRAPHY

The rapid expansion of AI research in echocardiography[18] underscores the field’s dynamic nature, making any classification of clinical value necessarily provisional. The contribution of AI can be conceptualized across four progressive levels [Table 4 and Supplementary Tables 1, 2], aligned with contemporary evaluation frameworks that emphasize clinical impact and accountability.[9] Level 1: Automation. AI enhances efficiency by performing repetitive tasks such as view recognition, acquisition guidance, and chamber quantification.[19] These systems reduce variability, shorten examination time, and provide real-time feedback, particularly supporting less experienced operators.[20,21,22,23] Level 2: Standardization. Automated EF and GLS assessment improves reproducibility across operators and vendors.[24,25] AI-guided probe positioning and image-quality scoring further reduce operator dependency, enhance acquisition reliability, and facilitate structured reporting for multicenter research and registries.[26,27] Level 3: Phenotyping and knowledge expansion. Beyond automation, AI enables data-driven discovery. Through radiomics, clustering, and deep phenotyping, algorithms identify disease-specific signatures and novel HFpEF subtypes with potential prognostic implications. Here, AI augments human interpretation by revealing patterns that the naked eye cannot readily discern. Level 4: Predictive and personalized care. Integrating imaging with clinical and multimodal data, AI supports outcome prediction and individualized risk stratification.[28] Emerging models can even infer biological or “echo-derived” heart age directly from routine imaging,[29] illustrating the shift from descriptive imaging to precision-oriented decision support.

Table 4.

Governance-oriented artificial intelligence maturity levels for echocardiography laboratories

Level		Descriptor		Technology adoption		Workflow automation		Governance and validation		Digital readiness
Level 0		No AI adoption		No AI tools; conventional systems		0% automation; fully manual		No governance or validation		Low
Level 1		Initial exposure		Single-tool automation		≤10% automation; full human verification		No formal validation		Low–moderate
Level 2		Local integration		Multiple AI modules		10%–30% automation		Informal checks; limited interoperability		Moderate
Level 3		Structured implementation		AI in PACS/structured reporting		30%–60% automation		Local validation; QA alignment		Moderate–high
Level 4		Routine AI-enabled workflow		Interoperable AI suite		60%–90% automation		Continuous audit trail; governance structure		High
Level 5		Full AI-driven workflow		End-to-end AI platform		≥90% automation		Human governance; accreditation-ready		Very high

AI=Artificial intelligence, PACS=Picture archiving and communication system, QA=Quality assurance

Supplementary Table 1.

Artificial intelligence-enabled echocardiography: Representative studies across core clinical domains

Parameter/task		Algorithm type		Study design and dataset		Main findings (performance metrics)		Clinical domain/application		Reference
AI-driven quantitative automation
LV structure and systolic function		2D multistep ML pipeline		Multi-view datasets (A4C/A2C) with internal and external validation (n≈1000–60,000 studies)		Automated LV volumes and LVEF showed MAE 4%–10%, correlation coefficients 0.75–0.95; time reduction up to 70% versus manual tracing		Automation/standardization (volume and EF quantification)		[1,2,3]
Fully automated LV mass quantification		DL ensemble (multiview + multimodality calibration)		Prospective multimodality validation; 218 paired TTE–CT studies; reproducibility substudy (n=40); calibration cohort (n=41)		ICC up to 0.76 versus expert LV mass; mean absolute percentage error 0.16; AUC 0.84 for LV hypertrophy classification; reduced interobserver variability compared with manual tracings		Automated, standardized LV mass assessment with cross-modality validation		[4]
LVEF (end-to-end CNN)		DL (CNN/transformer)		End-to-end analysis of apical 2–3 view loops, trained on >10,000 images		MAE 4%–6%; ICC 0.85–0.95; accuracy comparable to experts; reproducibility superior to manual		Automation/efficiency (real-time EF estimation, workflow acceleration)		[5,6,7]
GLS		ML-based motion tracking		Fully automated tracking of apical views; external validation across pathologies		Mean absolute difference 1%–3%; strong correlation (R 0.85–0.93); excellent reproducibility		Standardization/reproducibility (automated strain quantification)		[8,9,10]
LV diastolic indices		Multistep regression models		Doppler- and TDI-based automated E/e′ and e′ estimation		MAE 1–2; R 0.8–0.95; agreement with manual assessment; potential for large-scale screening		Automation/large-scale screening (diastolic function indices)		[1,11]
LA structure and function		Multistep ML/end-to-end DL		Automated 2D/3D LA volume and strain analysis		MAE 5–10 mL for volume; 2%–4% for strain; reproducibility comparable to experts		Phenotyping/prognosis (LA function as biomarker)		[1,12]
RV function		End-to-end CNN		2D A4C models predicting 3D- or CMR-derived RVEF		Bias 4%–6%; correlation 0.65–0.75; potential to approximate 3D accuracy from 2D loops		Automation/cross-modality translation (2D to 3D equivalence)		[13,14]
AI-based acquisition guidance and automated interpretation
Real-time acquisition guidance for novices		DL (real-time CNN guidance module)		Prospective randomized study; novices performing TTE with AI assistance		+ 34% increase in diagnostic-quality views; >90% of apical views rated “adequate”; acquisition time reduced by ~25%		Acquisition guidance; novice training; POCUS		[15]
Real-time AI-based novice guidance image quality suitability		DL-based real-time acquisition guidance		Prospective observational study; novices acquiring parasternal and apical views		Diagnostic adequacy improved from 52% → 84%; suitability for quantitative analysis increased 2.1-fold; inter-operator variability reduced by ~30%		POCUS; acquisition standardization		[15]
Comprehensive automated interpretation (EF, GLS, volumes)[15]		Multitask DL (view classification + segmentation + quantification)		Multicenter (5 sites), multi-vendor dataset; external validation		EF mean absolute error 3.4%; GLS MAE 2.1%; LVEDV/LVESV correlation r>0.92 versus expert readers; interpretation time reduced from 14 → 4 min		Automated full-echo interpretation		[16]
AI-based disease detection
HFpEF detection (AI-HFpEF model)		3D CNN		Retrospective, multisite (Mayo Clinic + St George’s); training n=6756; independent test n=1284		AUC 0.97 (train), 0.95 (val), 0.93 (test); sensitivity 87.8%, specificity 81.9%; reclassification of 73% indeterminate HFA-PEFF/H2FPEF		Automated diagnosis from A4C; improved accuracy versus clinical scores		[17]
HFpEF screening (ML ensemble)		ML Ensemble		Retrospective multicenter cohort		AUC 0.85–0.90 for HFpEF versus non-HFpEF		HFpEF screening and risk identification		[17]
Cardiac amyloidosis detection (AI-CA)		Ensemble of 3D CNNs		Retrospective, multicentre, multiethnic; n=2612; external n=2719		AUC 0.93; sensitivity 85%; specificity 93%; outperforming TCAS and IWT		ATTR/AL amyloidosis screening from single A4C		[18]
Amyloidosis single-clip screening		Video CNN		Large echo-video dataset		AUC >0.90; robust across vendors		Automated ATTR screening		[19]
HCM diagnosis (ML-HCM)		Random forest		Large registry (n=22,521)		AUC 0.92–0.98; sensitivity 87%–97%; specificity 83%–98%; strain improved performance		HCM detection using standard echo		[20]
Under-recognized cardiomyopathies on POCUS[19,20]		Multi-label CNN		Prospective multicentre; >90,000 POCUS videos		AUC 0.88–0.92 across subtypes; earlier diagnosis (~2 years)		Opportunistic screening in ED and low-resource settings		[21]
AI-based coronary disease
Detection of severe CAD from echocardiography		Deep learning (CNN-based video analysis)		Retrospective, multi-center dataset; TTE clips benchmarked against ICA-defined CAD severity		AUC≈0.90 for detecting severe CAD; sensitivity 80%–85%, specificity 80%–90%; consistent performance across apical views		CAD detection; automated triage		[22]
Automated detection of RWMA compared to human readers		Deep learning (3D CNN + supervised classification)		Prospective multi-reader, multi-case study; echo videos compared with expert consensus RWMA labels		Model AUC≈0.85–0.88; outperformed median human reader; reduced inter-reader variability by~20%–30%		Regional wall motion abnormality detection; resting/stress echo interpretation		[20]
AI-based automation of valvular heart disease assessment
MR: Multiparametric ML-based quantification		Supervised ML (ensemble models integrating structural + Doppler features)		Clinical MR cohort with expert-labeled severity; internal and external validation		Automated multiparametric grading showed good agreement with expert MR severity (AUC≈0.90); improved reproducibility vs single-parameter assessments		Automation/standardized MR grading		[23]
High-throughput MR detection		DL (CNN applied to full cine-loops)		Large-Scale dataset (>30,000 studies); multi-view DL pipeline		AUC≈0.97 for moderate/severe MR; robust across vendors		Screening/early detection of MR		[24]
MR characterization and flow tracking (DELINEATE-MR)		End-to-end DL (CNN + optical flow)		Multicenter echo registry; >10,000 studies of MR; Doppler flow tracking		Automated MR jet characterization and quantification; high correlation with CMR; AUC≈0.94		Comprehensive MR assessment/advanced phenotyping		[25]
Clustering of primary MR phenotypes using XAI		Unsupervised ML + XAI (clustering + SHAP)		Primary degenerative MR cohort; comprehensive echo parameters; unsupervised derivation		Identification of four distinct MR phenotypes with different outcomes; XAI revealed structural drivers		Phenotyping/precision stratification		[26]
AS: DL-based detection		Deep learning (CNN/Transformer) applied to multi-view echo		Echo dataset of AS patients across all severities; external validation in independent cohort		Automated detection of severe AS with AUC≈0.97; PPV≈0.91; NPV≈0.96		Screening/automated identification of severe AS		[25]
Multivalvular regurgitation (AR/MR/TR)–DELINEATE-regurgitation		End-to-end DL (multi-view CNN and temporal analysis)		71 660 TTEs; 1.2 million color Doppler videos; two-centre validation		Weighted κ: AR 0.81/0.76, MR 0.76/0.72, TR 0.73/0.64; progression risk score HR 4.1		Automation + prognostic stratification across valves		[25]

A4C=Apical four-chamber, AI=Artificial intelligence, AR=Aortic regurgitation, AS=Aortic stenosis, ATTR=Transthyretin amyloidosis, AUC=Area under the curve, CAD=Coronary artery disease, CA=Cardiac amyloidosis, CMR=Cardiac magnetic resonance, CNN=Convolutional neural network, DL=Deep learning, ED=Emergency department, EF=Ejection fraction, GLS=Global longitudinal strain, HFpEF=Heart failure with preserved EF, HFA-PEFF=Heart failure association pretest assessment, H₂FPEF=Heavy, hypertensive, atrial fibrillation, pulmonary hypertension, elder, filling pressure, HCM=Hypertrophic cardiomyopathy, ICA=Invasive coronary angiography, ICC=Intraclass correlation coefficient, LV=Left ventricular, LA=Left atrial, IWT=Increased wall thickness, MAE=Mean absolute error, ML=Machine learning, MR=Mitral regurgitation, NPV=Negative predictive value, POCUS=Point-of-care ultrasound, PPV=Positive predictive value, RWMA=Regional wall motion abnormality, RV=Right ventricular, TCAS=Transthyretin cardiac amyloidosis score, TR=Tricuspid regurgitation, TTE=Transthoracic echocardiography, XAI=Explainable AI, 3D=Three dimensional, SHAP=Shapley additive explanations, LVEF=Left ventricular EF, 2D=Two dimensional, LVEDV=Left ventricular end-diastolic volume, CT=Computed tomography, HR=Hazard ratio, LVESV=Left ventricular end-systolic volume, RVEF=Right ventricular EF

Supplementary Table 2.

Commercially available artificial intelligence tools for echocardiographic quality assurance and acquisition guidance

Vendor/system		AI task		Tool/module		Functionality		Regulatory status
Philips (EPIQ/affiniti/lumify)		View recognition		Auto view recognition		Automatic identification and labeling of standard views		FDA-cleared/CE-marked
		Acquisition guidance		Real-time quality feedback		Real-time prompts for probe angle, depth, alignment; assessment of image adequacy		FDA-cleared/CE-marked
		Dataset quality control		DHM QC flags		QC indicators for segmentation stability and tracking reliability		FDA-cleared/CE-marked
GE healthcare (vivid line+caption AI)		View recognition		Caption AI–view classification		Automated classification for>14 echocardiographic views		FDA 510(k)
		Acquisition guidance		Caption guidance		Real-time instructions for probe motion; automated acceptance/rejection (“quality gate”)		FDA 510(k)
		Dataset quality control		AutoEF QC		Automated QC on LV segmentation consistency		FDA-cleared
Siemens healthineers (ACUSON sequoia, SC2000)		View recognition		eSie AI view classification		AI-based real-time view identification		CE-marked
		Acquisition guidance		ACUSON assist AI		Guidance for view alignment, depth, and centering		CE-marked
		Dataset quality control		Multichamber AI segmentation QC		QC flags for 3D LV/LA/RV segmentation		CE-marked
Canon medical systems		View recognition		Smart view		Automated labeling of cardiac views		CE-marked
		Dataset quality control		3D wall motion tracking QC		Rejects unstable frames; evaluates tracking stability		CE-marked
Samsung (V7, V8, V9)		View recognition		HeartAssist™–view assist		Automatic view labeling		CE-marked
		Acquisition guidance		Real-time guidance		Real-time suggestions for improved angulation, depth, centering		CE-marked
Vendor-neutral platforms		View recognition		Us2.ai–view classification		Automated detection of all standard echo views		CE-marked
		Dataset quality control		Us2.ai QC layer		QC rejection of poor-quality loops; resubmission prompts		CE-marked
		Acquisition guidance		Caption AI cloud integration		Cloud-based real-time acquisition feedback		FDA-cleared

3D=Three-dimensional, AI=Artificial intelligence, CE=Conformity European, DHM=Dynamic heart model, EF=Ejection fraction, FDA=Food and drug administration, LA=Left atrium, LV=Left ventricle, QC=Quality control, RV=Right ventricle

Together, these levels describe a continuum from automation to augmentation. However, technological progress has outpaced clinical validation: most tools remain supported by retrospective or internally validated studies, with limited prospective and endpoint-driven evidence.[30] External validation in intended-use settings and AI-specific methodological standards remain essential before widespread clinical adoption can be justified.

BRINGING ARTIFICIAL INTELLIGENCE FROM PROTOTYPE TO PRACTICE

Certified AI systems for echocardiography are deployed as regulated, medical-grade solutions for hospitals and imaging laboratories rather than consumer applications. Implementation typically follows three models: Cloud-based services, on-premises installations integrated with PACS or vendor workstations, or direct embedding into ultrasound platforms, each operating under established regulatory and data-protection frameworks (e.g., the Health Insurance Portability and Accountability Act and the General Data Protection Regulation). These systems function as clinical workflow tools, not educational chatbots, and currently represent the primary mode of AI integration in routine echocardiographic practice.

Beyond commercial solutions, a complementary pathway is emerging through regulated, open-source imaging ecosystems. Platforms such as PACS-AI[31] demonstrate how audit-ready infrastructure enables institutions to integrate validated AI modules into their local environments, promoting governance, reproducibility, and vendor-neutral interoperability. In parallel, professional societies may formalize AI maturity standards into structured digital accreditation pathways, analogous to initiatives such as the American College of Radiology ARCH-AI program,[32] to ensure transparent governance and consistent implementation across echocardiography laboratories.

SUSTAINABILITY AND GLOBAL EQUITY

AI in echocardiography should be evaluated not only by its accuracy but also by its sustainability and accessibility.[33] By reducing repeat studies, harmonizing workflows, and enabling remote acquisition, AI can optimize resource use and even lower the carbon footprint of cardiovascular imaging.[34] Beyond efficiency, AI also carries a moral dimension, as it can democratize access to high-quality imaging.[35] Cloud-based platforms enable the upload of echocardiographic data acquired in remote or resource-limited settings for automated analysis and processing.[36] A community health worker in a rural clinic could obtain a basic dataset, transmit it securely, and receive standardized measurements or a preliminary structured report. These innovations align with hub-and-spoke care models, where peripheral centers perform acquisition while tertiary hubs oversee quality assurance and clinical integration. Similarly, task shifting, empowering trained community workers with AI-guided acquisition and cloud-based interpretation, can expand access where cardiologists are scarce. By bridging geographic and economic divides, AI may expand access to standardized echocardiographic assessment in underserved settings.[37]

THE (NEW) ROLE OF THE ECHOCARDIOGRAPHER IN THE AI ERA

The debate over whether AI might replace echocardiographers has been prominent,[38] but it reflects a false dichotomy. AI excels at pattern recognition and reproducible quantification, yet it cannot replicate clinical judgment, contextual reasoning, or patient-facing communication. As acquisition, quantification, and draft reporting become increasingly automated, the echocardiographer’s role evolves rather than diminishes: From measurer to interpreter, integrating AI-derived data with clinical context and multimodality information.[39] Clinicians ultimately remain responsible for adaptability, synthesis, and accountability, overseeing teams in which AI improves consistency while expert supervision safeguards quality.[40] Above all, the echocardiographer serves as the guardian of trust, ensuring that AI-derived outputs are reliable, interpretable, and clinically meaningful.[41] Patients also seek reassurance, shared decision-making, and continuity of care, elements that no algorithm, albeit sophisticated, can authentically provide.[38] Two metaphors capture this partnership [Figure 2]. The centaur represents collaboration, with distinct roles: AI performs repetitive tasks, such as segmentation or strain analysis, while the clinician exercises judgment. The cyborg represents a seamless fusion: Human and AI co-creating the report in real-time.[42] The art of the future echocardiographer will be adaptability, switching between these modes as context demands. AI will not replace but transform the echocardiographer, from technician to clinical supervisor, from measurer to integrator, from solitary performer to trusted leader [Figure 3].

Figure 2.

Interaction styles between clinicians and AI in echocardiography. In the Centaur mode, AI performs repetitive tasks while the clinician supervises and interprets. In the Cyborg mode, human-AI integration is tighter, with co-constructed reports and real-time feedback. Echocardiographers must adaptively switch between modes according to task complexity and clinical risk

Figure 3.

Clinical leadership in AI-enabled echocardiography. Interpretation, integration, supervision, accountability, trust stewardship, and patient communication define the human role in AI-integrated workflows

REDEFINING SKILL SETS AND PROFESSIONAL BOUNDARIES IN THE ARTIFICIAL INTELLIGENCE ERA

The rise of AI in echocardiography is reshaping professional roles, particularly for sonographers and physicians, toward more collaborative and specialized workflows.[43] AI-assisted systems now guide image acquisition and automate measurements with greater precision, improving efficiency, diagnostic consistency, and reducing operator fatigue.[44,45,46,47] As these systems mature, sonographers are evolving into pivotal roles focused on quality assurance, workflow optimization, and human–machine interaction. However, sustainable integration of AI depends on preserving the complementary strengths of each actor: Sonographers ensuring acquisition quality, AI guaranteeing standardization, and clinicians maintaining interpretive and procedural mastery.

TRANSFERABILITY OF AI IN ECHOCARDIOGRAPHY: FROM PROOF-OF-CONCEPT TO REAL-WORLD PRACTICE

AI in echocardiography holds great promise; however, its translation from research to clinical routine requires the same vigilance that clinicians already apply to new drugs [Table 5]. Accountability remains a key issue: If an AI-generated report contributes to harm, responsibility may extend to both developers and institutions. Data governance is equally sensitive, as patients increasingly object to the reuse or commercialization of their medical data, even when anonymized. Clinicians must not mistake regulatory approval for clinical maturity.[48] Just as pharmacovigilance ensures postmarketing surveillance of adverse events, AI-vigilance should be integrated with clinical adoption. A certified algorithm must still prove consistent performance across populations, vendors, and laboratories. AI literacy, therefore, is not about decoding the black box, but about maintaining the same critical skepticism used for any therapeutic tool: Trust, but verify.[49] Economic sustainability and technical robustness are equally essential. Efficiency gains may be offset by acquisition and maintenance costs, while overfitting, propagation bias, and adversarial manipulations can compromise validity. The recently approved European Artificial Intelligence Act (AI Act)[50] is the first comprehensive regulatory framework for AI in the European Union. Although medical AI is already regulated under the Medical Device Regulation (MDR), the new EU AI Act introduces safeguards that are far more specific to data-driven diagnostic systems. For echocardiography, nearly all AI tools fall under the high-risk category, requiring documented dataset quality, transparency, explainability, and continuous postmarket monitoring, elements only partially addressed by the MDR. The AI Act also mandates mechanisms to detect model drift and ensure human oversight, acknowledging that the performance of DL algorithms may change over time and across vendors or populations. In this sense, the AI Act complements and extends the MDR, providing a regulatory framework better aligned with the dynamic nature of AI-based echocardiography.[51]

Table 5.

Artificial intelligence-assisted echocardiographic approach: transferability in healthcare

Potential issue		Measures
Overreliance on technology		Users must remain critical
Legislative and legal		AI products classified as medical devices
Accountability and reliability		Postmarket safety monitoring
Efficacy–effectiveness gap		Compare outcomes versus standard practice
Data protection		Cybersecurity safeguards
Financial costs		New reimbursement models
Lack of standardized guidelines		Development of regulatory frameworks
Black-box nature of the dataset		External validation required
Overfitting		Regulatory agencies must require labeling and validation
Propagation bias		Debiasing
Vendor differences		Task forces for harmonization
Healthcare disparities		Hub-and-spoke networking, equitable deployment

AI=Artificial intelligence

TEACHING ECHOCARDIOGRAPHY IN THE ARTIFICIAL INTELLIGENCE ERA

Integrating AI into echocardiography requires not only technological advancements but also a rethinking of education.[52] A helpful metaphor is the “AI license,” like a driver’s license, built on three pillars: Structured curricula for trainees, training programs for educators, and institutional governance to validate and monitor AI tools. Competence should evolve in a stepwise manner: From basic literacy in medical school to supervised use during fellowship, to faculty-level oversight and institutional responsibility. Within this framework, AI can accelerate learning. Automated tools allow students to recognize views or perform strain analysis early in training. Yet premature reliance on automation risks never-skilling (skills never acquired), mis-skilling (incorrect habits reinforced), and deskilling (progressive loss of competence).[42,53] As automation becomes more pervasive, there is a risk that clinicians’ alertness and technical skills gradually erode, and increasing dependence on AI may inadvertently weaken confidence in one’s own independent clinical judgment, which is the essence of echocardiographic expertise. However, deskilling is not an unavoidable consequence of AI. Other high-reliability industries offer clear precedents: When faced with similar risks, sectors such as aviation and nuclear operations introduced structured strategies to preserve core competencies despite widespread automation.[53] Pilots, for example, routinely perform manual flight time and simulator exercises designed around system-failure scenarios. Comparable approaches, periodic hands-on imaging, supervised refreshers, and simulation-based practice, may help safeguard physicians’ technical proficiency as AI becomes embedded in everyday echocardiographic workflows.

Learning in the AI era must also become bidirectional. Many younger trainees are more digitally fluent than their mentors; hence, shared learning environments are essential for teachers and learners to evolve together. The DEFT-AI framework (Discussion, Evidence, Feedback, Teaching) offers a practical scaffold for this transformation,[42] promoting structured reflection, supervised experimentation, and safe integration of AI into daily clinical education [Figure 4].

Figure 4.

The DEFT-AI framework for echocardiography education. D: Diagnosis/Discussion; E: Evidence; F: Feedback; T: Teaching

In essence, effective teaching requires fostering adaptive practice, balancing efficiency with scrutiny when AI is uncertain, because only reasoning, not automation, safeguards quality. The choices we make in training and deploying AI today will define its impact on tomorrow’s echocardiography.

GENERATIVE ARTIFICIAL INTELLIGENCE IN ECHOCARDIOGRAPHY: ENTERING A REALM THAT RECENTLY SEEMED UNREAL

Generative artificial intelligence (GenAI) comprises models that learn the statistical distribution of existing data to generate synthetic yet realistic outputs.[54] Unlike traditional AI, which focuses on classification or prediction, GenAI creates new images, cine loops, or structured text. In echocardiography, its applications remain exploratory but include data augmentation to address scarcity and improve robustness, synthesis of rare pathologies through generative adversarial networks or diffusion models,[55] image-to-image translation for cross-modality validation, anomaly detection, and automated report drafting using large language models.[56] Emerging directions such as radiomics,[57] multimodal integration,[58] and digital twins[59] may further expand its role.

However, GenAI introduces specific risks, including hallucinated but plausible anatomy and amplification of dataset bias.[60] Accordingly, it should currently be regarded as a hypothesis-generating or workflow-support tool, requiring task-specific validation, uncertainty assessment, and careful governance before clinical deployment.[9] Its future integration will depend on transparent evaluation and explicit acknowledgment of its limitations.[61]

FROM ALGORITHM TO BEDSIDE: LIMITATIONS AND UNMET NEEDS

Despite rapid progress, substantial heterogeneity persists across vendors, acquisition systems, and postprocessing platforms.[62,63] Although AI may harmonize analysis, variability originating from image acquisition continues to influence both automated and manual measurements. Approaches such as domain adaptation and federated training[64,65] aim to enhance cross-platform robustness, yet their generalizability remains incompletely validated. External multicentre testing and real-world variability assessment are therefore essential. In addition, the conceptual frameworks proposed in this review, including the clinical value continuum and AI maturity model, have not undergone formal empirical validation. They are presented as scaffolding for structured discussion and future testing rather than definitive classifications. Their ultimate utility will depend on prospective evaluation, reproducibility across heterogeneous practice settings, and potential integration into accreditation and guideline frameworks. Ultimately, adoption will require not only technical performance but demonstrable clinical impact, regulatory alignment, and sustainable workflow integration.

CONCLUSIONS

AI is no longer an adjunct to echocardiography but a force reshaping its standards of acquisition, interpretation, and governance. Beyond automation, it enables phenotyping, prognostic modeling, and precision-oriented decision support. Yet technical performance alone cannot justify adoption: Rigorous external validation, transparent reporting, regulatory oversight, and postdeployment monitoring remain essential. The transition from literacy to leadership is therefore imperative. Echocardiographers, professional societies, industry, and regulators must actively shape validation standards, accreditation pathways, and governance frameworks. If guided by principled stewardship, AI can enhance diagnostic consistency, reduce variability, and expand equitable access to high-quality echocardiography worldwide. Its ultimate impact will depend less on algorithmic complexity than on the standards and clinical judgment governing its use.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.