{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:12:23Z","timestamp":1784542343949,"version":"3.55.0"},"reference-count":78,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T00:00:00Z","timestamp":1759795200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Echocardiography is a first-line, non-invasive imaging modality widely used to assess cardiac structure and function; however, its interpretation remains highly operator dependent and subject to variability. The integration of artificial intelligence (AI) into echocardiographic practice holds the potential to transform workflows, enhance efficiency, and improve the consistency of assessments across diverse clinical settings. Interest in the application of AI to echocardiography has grown significantly since the early 2000s with AI models that assist with image acquisition, disease detection, measurement automation, and prognostic stratification for various cardiac conditions. Despite this momentum, the safe and effective deployment of AI models relies on rigorous development and validation practices, yet these are infrequently described in the literature. This narrative review aims to provide a comprehensive overview of the essential steps in the development and validation of AI models for echocardiography. Additionally, it explores current challenges and outlines future directions for the integration of AI within echocardiography.<\/jats:p>","DOI":"10.3390\/jcm14197066","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T10:48:07Z","timestamp":1759834087000},"page":"7066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5189-3513","authenticated-orcid":false,"given":"Sadie","family":"Bennett","sequence":"first","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"},{"name":"Heart & Lung Clinic, University Hospitals of North Midlands NHS Trust, Stoke-on-Trent ST4 6QG, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6281-3523","authenticated-orcid":false,"given":"Casey L.","family":"Johnson","sequence":"additional","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4661-9516","authenticated-orcid":false,"given":"George","family":"Fisher","sequence":"additional","affiliation":[{"name":"Department of Cardiology, King\u2019s College Hospital, London SE5 9RS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3432-0006","authenticated-orcid":false,"given":"Fiona","family":"Erskine","sequence":"additional","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3671-6978","authenticated-orcid":false,"given":"Samuel","family":"Krasner","sequence":"additional","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"},{"name":"John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2182-1089","authenticated-orcid":false,"given":"Andrew J.","family":"Fletcher","sequence":"additional","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"},{"name":"Department of Cardiology, Royal Papworth Hospital NHS Foundation Trust, Cambridge CB2 0AY, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-9297","authenticated-orcid":false,"given":"Paul","family":"Leeson","sequence":"additional","affiliation":[{"name":"Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK"},{"name":"John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"034002","DOI":"10.1117\/1.JMI.8.3.034002","article-title":"Neural Architecture Search of Echocardiography View Classifiers","volume":"8","author":"Azarmehr","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref_2","first-page":"3","article-title":"The Use of Echocardiography in Acute Cardiovascular Care: Recommendations of the European Association of Cardiovascular Imaging and the Acute Cardiovascular Care Association","volume":"4","author":"Lancellotti","year":"2015","journal-title":"Eur. Heart J. Acute Cardiovasc. Care"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, L., Tu, B., Wang, J., He, Y., Jiang, R., Wu, X., Wen, W., and Liu, J. (2025). Application of Artificial Intelligence in Echocardiography from 2009 to 2024: A Bibliometric Analysis. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1587364"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e188","DOI":"10.7861\/fhj.2021-0095","article-title":"Artificial Intelligence in Healthcare: Transforming the Practice of Medicine","volume":"8","author":"Bajwa","year":"2021","journal-title":"Future Heal. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine Learning: Trends, Perspectives, and Prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1016\/j.jacc.2018.12.054","article-title":"Artificial Intelligence in Cardiovascular Imaging","volume":"73","author":"Dey","year":"2019","journal-title":"JACC"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e016148","DOI":"10.1161\/CIRCIMAGING.123.016148","article-title":"Artificial Intelligence to Speed Up Training in Echocardiography: The Next Frontier","volume":"16","author":"Meucci","year":"2023","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1016\/j.jcmg.2021.10.013","article-title":"Automated Echocardiographic Detection of Severe Coronary Artery Disease Using Artificial Intelligence","volume":"15","author":"Upton","year":"2022","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100452","DOI":"10.1016\/j.jacadv.2023.100452","article-title":"Automated Echocardiographic Detection of Heart Failure With Preserved Ejection Fraction Using Artificial Intelligence","volume":"2","author":"Akerman","year":"2023","journal-title":"JACC Adv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.compbiomed.2016.03.026","article-title":"Automatic Assessment of Mitral Regurgitation Severity Based on Extensive Textural Features on 2D Echocardiography Videos","volume":"73","author":"Moghaddasi","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1016\/j.jcmg.2021.04.018","article-title":"Artificial Intelligence for Automatic Measurement of Left Ventricular Strain in Echocardiography","volume":"14","author":"Salte","year":"2021","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e011951","DOI":"10.1161\/CIRCIMAGING.120.011951","article-title":"Automated Left Ventricular Dimension Assessment Using Artificial Intelligence Developed and Validated by a UK-Wide Collaborative","volume":"14","author":"Howard","year":"2021","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1038\/s41586-023-05947-3","article-title":"Blinded, Randomized Trial of Sonographer versus AI Cardiac Function Assessment","volume":"616","author":"He","year":"2023","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Barry, T., Farina, J.M., Chao, C.-J., Ayoub, C., Jeong, J., Patel, B.N., Banerjee, I., and Arsanjani, R. (2023). The Role of Artificial Intelligence in Echocardiography. J. Imaging, 9.","DOI":"10.3390\/jimaging9020050"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ferraz, S., Coimbra, M., and Pedrosa, J. (2023). Assisted Probe Guidance in Cardiac Ultrasound: A Review. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1056055"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"qyad029","DOI":"10.1093\/ehjimp\/qyad029","article-title":"A Machine Learning-Based Score for Precise Echocardiographic Assessment of Cardiac Remodelling in Hypertensive Young Adults","volume":"1","author":"Alsharqi","year":"2023","journal-title":"Eur. Heart J.-Imaging Methods Pract."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.echo.2020.04.025","article-title":"Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers","volume":"33","author":"Davis","year":"2020","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/pd.5892","article-title":"Artificial Intelligence, Fetal Echocardiography, and Congenital Heart Disease","volume":"41","author":"Day","year":"2021","journal-title":"Prenat. Diagn."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"An, Q., Rahman, S., Zhou, J., and Kang, J.J. (2023). A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors, 23.","DOI":"10.3390\/s23094178"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/ehjdh\/ztac016","article-title":"A Clinician\u2019s Guide to Understanding and Critically Appraising Machine Learning Studies: A Checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML)","volume":"3","author":"Alghwiri","year":"2022","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100153","DOI":"10.1016\/j.jacadv.2022.100153","article-title":"Artificial Intelligence in Congenital Heart Disease","volume":"1","author":"Jone","year":"2022","journal-title":"JACC Adv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1053\/j.sodo.2024.01.007","article-title":"Missing Data: Issues, Concepts, Methods","volume":"30","author":"Pham","year":"2024","journal-title":"Semin. Orthod."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102587","DOI":"10.1016\/j.artmed.2023.102587","article-title":"Handling Missing Values in Healthcare Data: A Systematic Review of Deep Learning-Based Imputation Techniques","volume":"142","author":"Liu","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.jclinepi.2021.11.023","article-title":"Missing Data Is Poorly Handled and Reported in Prediction Model Studies Using Machine Learning: A Literature Review","volume":"142","author":"Nijman","year":"2022","journal-title":"J. Clin. Epidemiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"328","DOI":"10.4103\/1658-354X.174918","article-title":"Basic Concepts for Sample Size Calculation: Critical Step for Any Clinical Trials!","volume":"10","author":"Gupta","year":"2016","journal-title":"Saudi J. Anaesth."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_28","unstructured":"U.S. Food and Drug Administration (2025, July 26). Medicines and Healthcare Productes Good Machine Learning Practice for Medical Device Development: Guiding Principles, Available online: https:\/\/www.gov.uk\/government\/publications\/good-machine-learning-practice-for-medical-device-development-guiding-principles."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"m3164","DOI":"10.1136\/bmj.m3164","article-title":"Reporting Guidelines for Clinical Trial Reports for Interventions Involving Artificial Intelligence: The CONSORT-AI Extension","volume":"370","author":"Liu","year":"2020","journal-title":"BMJ"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s44325-025-00064-8","article-title":"Contemporary Applications of Artificial Intelligence and Machine Learning in Echocardiography","volume":"2","author":"Xu","year":"2025","journal-title":"npj Cardiovasc. Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"81","DOI":"10.3390\/geomatics4010005","article-title":"Ground Truth in Classification Accuracy Assessment: Myth and Reality","volume":"4","author":"Foody","year":"2024","journal-title":"Geomatics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1016\/j.ultrasmedbio.2022.11.003","article-title":"van Ultrasound Signal Processing: From Models to Deep Learning","volume":"49","author":"Luijten","year":"2023","journal-title":"Ultrasound Med. Biol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.ultrasmedbio.2018.07.024","article-title":"Real-Time Standard View Classification in Transthoracic Echocardiography Using Convolutional Neural Networks","volume":"45","author":"Ostvik","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.4338\/ACI-2016-03-SOA-0035","article-title":"Preprocessing Structured Clinical Data for Predictive Modeling and Decision Support","volume":"7","author":"Oliveira","year":"2016","journal-title":"Appl. Clin. Inf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ejmp.2021.02.007","article-title":"Data Preparation for Artificial Intelligence in Medical Imaging: A Comprehensive Guide to Open-Access Platforms and Tools","volume":"83","author":"Diaz","year":"2021","journal-title":"Phys. Medica"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41746-023-00773-3","article-title":"The Impact of Inconsistent Human Annotations on AI Driven Clinical Decision Making","volume":"6","author":"Sylolypavan","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_37","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e113","DOI":"10.1016\/S2589-7500(24)00249-8","article-title":"Artificial Intelligence-Guided Detection of under-Recognised Cardiomyopathies on Point-of-Care Cardiac Ultrasonography: A Multicentre Study","volume":"7","author":"Oikonomou","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A Comparison of Deep Learning Performance against Health-Care Professionals in Detecting Diseases from Medical Imaging: A Systematic Review and Meta-Analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e46","DOI":"10.1016\/S2589-7500(21)00235-1","article-title":"Automated Interpretation of Systolic and Diastolic Function on the Echocardiogram: A Multicohort Study","volume":"4","author":"Tromp","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1016\/j.echo.2022.06.013","article-title":"Unsupervised Machine Learning for Assessment of Left Ventricular Diastolic Function and Risk Stratification","volume":"35","author":"Chao","year":"2022","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-Based AI for Beat-to-Beat Assessment of Cardiac Function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"ref_43","first-page":"27","article-title":"Impact of Image Quality on Reliability of the Measurements of Left Ventricular Systolic Function and Global Longitudinal Strain in 2D Echocardiography","volume":"5","author":"Nagata","year":"2018","journal-title":"Echo Res. Pr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.jcmg.2015.12.020","article-title":"Transthoracic 3D Echocardiographic Left Heart Chamber Quantification Using an Automated Adaptive Analytics Algorithm","volume":"9","author":"Tsang","year":"2016","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.jcmg.2018.04.026","article-title":"Predicting Survival From Large Echocardiography and Electronic Health Record Datasets: Optimization With Machine Learning","volume":"12","author":"Samad","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, M., Deng, Y., Liu, Y., Su, X., and Zeng, X. (2023). Echocardiography-Based Machine Learning Algorithm for Distinguishing Ischemic Cardiomyopathy from Dilated Cardiomyopathy. BMC Cardiovasc. Disord., 23.","DOI":"10.1186\/s12872-023-03520-4"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Valsaraj, A., Kalmady, S.V., Sharma, V., Frost, M., Sun, W., Sepehrvand, N., Ong, M., Equilbec, C., Dyck, J.R.B., and Anderson, T. (2023). Development and Validation of Echocardiography-Based Machine-Learning Models to Predict Mortality. eBioMedicine, 90.","DOI":"10.1016\/j.ebiom.2023.104479"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"100129","DOI":"10.1016\/j.patter.2020.100129","article-title":"Extensions of the External Validation for Checking Learned Model Interpretability and Generalizability","volume":"1","author":"Ho","year":"2020","journal-title":"Patterns"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fletcher, A.J., Lapidaire, W., and Leeson, P. (2021). Machine Learning Augmented Echocardiography for Diastolic Function Assessment. Front. Cardiovasc. Med., 8.","DOI":"10.3389\/fcvm.2021.711611"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Singh, H., Mhasawade, V., and Chunara, R. (2022). Generalizability Challenges of Mortality Risk Prediction Models: A Retrospective Analysis on a Multi-Center Database. PLoS Digit. Health, 1.","DOI":"10.1371\/journal.pdig.0000023"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1093\/ehjdh\/ztad069","article-title":"Population Data-Based Federated Machine Learning Improves Automated Echocardiographic Quantification of Cardiac Structure and Function: The Automatisierte Vermessung Der Echokardiographie Project","volume":"5","author":"Morbach","year":"2024","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1093\/ehjdh\/ztad072","article-title":"External Validation of a Deep Learning Algorithm for Automated Echocardiographic Strain Measurements","volume":"5","author":"Myhre","year":"2024","journal-title":"Eur. Heart J. Digit. Health"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"AIoa2400865","DOI":"10.1056\/AIoa2400865","article-title":"PROTEUS: A Prospective RCT Evaluating Use of AI in Stress Echocardiography","volume":"1","author":"Upton","year":"2024","journal-title":"NEJM AI"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/j.jacc.2016.08.062","article-title":"Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography","volume":"68","author":"Narula","year":"2016","journal-title":"JACC"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1093\/eurheartj\/ehy531","article-title":"Prevalence and Correlates of Coronary Microvascular Dysfunction in Heart Failure with Preserved Ejection Fraction: PROMIS-HFpEF","volume":"39","author":"Shah","year":"2018","journal-title":"Eur. Heart J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.1093\/eurheartj\/ehaf248","article-title":"Deep Learning for Echocardiographic Assessment and Risk Stratification of Aortic, Mitral, and Tricuspid Regurgitation: The DELINEATE-Regurgitation Study","volume":"46","author":"Long","year":"2025","journal-title":"Eur. Heart J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.jjcc.2025.02.005","article-title":"AI-Echocardiography: Current Status and Future Direction","volume":"85","author":"Sahashi","year":"2025","journal-title":"J. Cardiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"e255","DOI":"10.1016\/j.landig.2025.02.003","article-title":"Snapshot Artificial Intelligence-Determination of Ejection Fraction from a Single Frame Still Image: A Multi-Institutional, Retrospective Model Development and Validation Study","volume":"7","author":"Malins","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","article-title":"A Quality Assessment Tool for Artificial Intelligence-Centered Diagnostic Test Accuracy Studies: QUADAS-AI","volume":"27","author":"Sounderajah","year":"2021","journal-title":"Nat. Med."},{"key":"ref_61","unstructured":"(2025, August 29). FDA Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, Available online: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-Performance Medicine: The Convergence of Human and Artificial Intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.3348\/kjr.2022.0834","article-title":"Randomized Clinical Trials of Artificial Intelligence in Medicine: Why, When, and How?","volume":"23","author":"Park","year":"2022","journal-title":"Korean J. Radiol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"101467","DOI":"10.1016\/j.conctc.2025.101467","article-title":"Reducing Inequalities through Greater Diversity in Clinical Trials\u2014As Important for Medical Devices as for Drugs and Therapeutics","volume":"45","author":"Roope","year":"2025","journal-title":"Contemp. Clin. Trials Commun."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1038\/s41746-025-01717-9","article-title":"A General Framework for Governing Marketed AI\/ML Medical Devices","volume":"8","author":"Babic","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_66","unstructured":"Dolin, P., Li, W., Dasarathy, G., and Berisha, V. (2025). Statistically Valid Post-Deployment Monitoring Should Be Standard for AI-Based Digital Health. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"405","DOI":"10.3348\/kjr.2019.0025","article-title":"Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers","volume":"20","author":"Kim","year":"2019","journal-title":"Korean J. Radiol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e034568","DOI":"10.1136\/bmjopen-2019-034568","article-title":"Reporting Quality of Studies Using Machine Learning Models for Medical Diagnosis: A Systematic Review","volume":"10","author":"Yusuf","year":"2020","journal-title":"BMJ Open"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.jcmg.2020.07.015","article-title":"Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist","volume":"13","author":"Shrestha","year":"2020","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_70","first-page":"1828","article-title":"A Responsible Framework for Applying Artificial Intelligence on Medical Images and Signals at the Point of Care: The PACS-AI Platform","volume":"40","author":"Corbin","year":"2024","journal-title":"Can. J. Cardiol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/j.acvd.2025.04.051","article-title":"Integrating Artificial Intelligence into an Echocardiography Department: Feasibility and Comparative Study of Automated versus Human Measurements in a High-Volume Clinical Setting","volume":"118","author":"Lafitte","year":"2025","journal-title":"Arch. Cardiovasc. Dis."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1148\/radiol.2020201434","article-title":"Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology","volume":"297","author":"Mollura","year":"2020","journal-title":"Radiology"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1001\/jama.2019.15064","article-title":"Potential Liability for Physicians Using Artificial Intelligence","volume":"322","author":"Price","year":"2019","journal-title":"JAMA"},{"key":"ref_74","unstructured":"Whicher, D., Ahmed, M., Israni, S.T., and Matheny, M. (2023). Artificial Intelligence Model Development and Validation. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril, National Academies Press."},{"key":"ref_75","first-page":"1","article-title":"Can Automation and Artificial Intelligence Reduce Echocardiography Scan Time and Ultrasound System Interaction?","volume":"12","author":"Hollitt","year":"2025","journal-title":"Echo Res. Pr."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.echo.2023.03.008","article-title":"Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography","volume":"36","author":"Krishna","year":"2023","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"jead119.387","DOI":"10.1093\/ehjci\/jead119.387","article-title":"Automated Analysis of Echocardiograms at Diagnosis Is Able to Predict Prognosis in ATTR Cardiomyopathy","volume":"24","author":"Ioannou","year":"2023","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine Learning in Medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N. Engl. J. Med."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/19\/7066\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:54Z","timestamp":1760035794000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/19\/7066"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,7]]},"references-count":78,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["jcm14197066"],"URL":"https:\/\/doi.org\/10.3390\/jcm14197066","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,7]]}}}