Artificial Intelligence for the Diagnosis of Heart Failure
Open accessAbstract
The diagnosis of heart failure can be difficult, even for heart failure specialists. Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CDSS for heart failure. AI-CDSS for cardiology was developed with a hybrid (expert-driven and machine-learning-driven) approach of knowledge acquisition to evolve the knowledge base with heart failure diagnosis. A retrospective cohort of 1198 patients with and without heart failure was used for the development of AI-CDSS (training dataset, n = 600) and to test the performance (test dataset, n = 598). A prospective clinical pilot study of 97 patients with dyspnea was used to assess the diagnostic accuracy of AI-CDSS compared with that of non-heart failure specialists. The concordance rate between AI-CDSS and heart failure specialists was evaluated. In retrospective cohort, the concordance rate was 98.3% in the test dataset. The concordance rate for patients with heart failure with reduced ejection fraction, heart failure with mid-range ejection fraction, heart failure with preserved ejection fraction, and no heart failure was 100%, 100%, 99.6%, and 91.7%, respectively. In a prospective pilot study of 97 patients presenting with dyspnea to the outpatient clinic, 44% had heart failure. The concordance rate between AI-CDSS and heart failure specialists was 98%, whereas that between non-heart failure specialists and heart failure specialists was 76%. In conclusion, AI-CDSS showed a high diagnostic accuracy for heart failure. Therefore, AI-CDSS may be useful for the diagnosis of heart failure, especially when heart failure specialists are not available.
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By integrating and analyzing patient symptoms, medical history, test results, and other risk factors, CDSS have the capability to support disease detection and identification.
“By integrating and analyzing patient symptoms, medical history, test results, and other risk factors, CDSS have the capability to support disease detection and identification [214,215], such as the subtle or ambiguous patterns that clinicians often overlook [216,217].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [217]
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McDonagh, T. A., Metra, M., Adamo, M., Gardner, R. S., Baumbach, A., Böhm, M., Burri, H., Butler, J., Čelutkienė, J., Chioncel, O. D., et al. (2021) · PubMed · doi:10.1093/eurheartj/ehab670 · Cited by 19,618 · see citing works
Lang, R., Badano, L., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F., Foster, E., Goldstein, S., Kuznetsova, T., et al. (2015) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.10.003 · Cited by 10,093 · see citing works
2016 ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure
Ponikowski, P., Voors, A. A., Anker, S. D., Bueno, H., Cleland, J. G. F., Coats, A. J. S., Falk, V., González-Juanatey, J. R., Harjola, V.-P., Jankowska, E. A., et al. (2016) · European Journal of Heart Failure · doi:10.1002/ejhf.592 · Cited by 5,866 · see citing works
Burden of Systolic and Diastolic Ventricular Dysfunction in the Community
Redfield, M. M., Jacobsen, S. J., Burnett, J. C. J., Mahoney, D. W., Bailey, K. R., & Rodeheffer, R. J. (2003) · JAMA · doi:10.1001/jama.289.2.194 · Cited by 2,989 · see citing works
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works
Bleumink, G. S., KNETSCH, A., Sturkenboom, M., Straus, S. E., Albert Hofman, D., Deckers, J. W., Witteman, J. C. M., & Stricker, B. H. C. (2004) · European Heart Journal · doi:10.1016/j.ehj.2004.06.038 · Cited by 1,134 · see citing works
More ‘Malignant’ Than Cancer? Five-Year Survival Following a First Admission for Heart Failure
Stewart, S., Macintyre, K., Hole, D. J., Capewell, S., & McMurray, J. J. V. (2001) · European Journal of Heart Failure · doi:10.1016/s1388-9842(00)00141-0 · Cited by 1,049 · see citing works
Reasoning Foundations of Medical Diagnosis
Ledley, R. S., & Lusted, L. B. (1959) · Science · doi:10.1126/science.130.3366.9 · Cited by 998 · see citing works
Electronic Alerts to Prevent Venous Thromboembolism among Hospitalized Patients
Kucher, N., Koo, S., Quiroz, R., Cooper, J. M., Paterno, M. D., Soukonnikov, B., & Goldhaber, S. Z. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa041533 · Cited by 942 · see citing works
Clinical decision support in the era of artificial intelligence
Shortliffe, E. H., & Sepúlveda, M. J. (2018) · JAMA · doi:10.1001/jama.2018.17163 · Cited by 920 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Somashekhar, S. P., Sepúlveda, M.-J., Puglielli, S. M., Norden, A. D., Shortliffe, E. H., Rohit Kumar, C., Rauthan, A., Kumar, N., Patil, P., Rhee, K. B. L., et al. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx781 · Cited by 366 · see citing works
Improving Blood Pressure Control through Provider Education, Provider Alerts, and Patient Education
Roumie, C. L., Elasy, T. A., Greevy, R. A., Griffin, M. R., Liu, X., Stone, W. J., Wallston, K. A., Dittus, R. S., Alvarez, V., Cobb, J., et al. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-3-200608010-00004 · Cited by 233 · see citing works
The Differential Diagnosis of Dyspnea
Berliner, D., Schneider, N., Welte, T., & Bauersachs, J. K. (2016) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2016.0834 · Cited by 169 · see citing works
Choi, D., Han, S., Jeon, E., Cho, M., Kim, J., Yoo, B., Shin, M., Seong, I. W., Ahn, Y., Kang, S., et al. (2011) · Korean Circulation Journal · doi:10.4070/kcj.2011.41.7.363 · Cited by 123 · see citing works
Self-citationReasoning Foundations of Medical Diagnosis
Ledley, R. S., & Lusted, L. B. (1985) · Computers and medicine · doi:10.1007/978-1-4613-8554-7_2 · Cited by 97 · see citing works
Korean Guidelines for Diagnosis and Management of Chronic Heart Failure
Kim, M., Lee, J., Kim, E. J., Park, D.-G., Park, S., Park, J. J., Shin, M., Yoo, B., Youn, J., Lee, S. E., et al. (2017) · Korean Circulation Journal · doi:10.4070/kcj.2017.0009 · Cited by 76 · see citing works
Self-citationKline, J. A., Zeitouni, R. A., Hernandez‐Nino, J., & Jones, A. E. (2009) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2008.09.034 · Cited by 55 · see citing works
Leg edema: clinical clues to the differential diagnosis.
Ciocon, J. O., Fernandez, B. B., & Ciocon, D. (1993) · PubMed · Cited by 43 · see citing works
Romero, C., Olmo, J. L., & Ventura, S. (2013) · Educational Data Mining · Cited by 31 · see citing works
Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax
Hussain, M., Afzal, M., Ali, T., Ali, R., Khan, W. A., Jamshed, A., Lee, S., Kang, B. H., & Latif, K. (2015) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2015.09.008 · Cited by 26 · see citing works
Self-citationMulti-model-based interactive authoring environment for creating shareable medical knowledge
Ali, T., Hussain, M., Khan, W. A., Afzal, M., Hussain, J., Ali, R., Hassan, W., Jamshed, A., Kang, B. H., & Lee, S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.07.010 · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Harnessing the Power of Clinical Decision Support Systems: Challenges and Opportunities
Chen, Z., Liang, N., Zhang, H., Li, H., Yang, Y., Zong, X., Chen, Y., Wang, Y., & Shi, N. (2023) · Open Heart · doi:10.1136/openhrt-2023-002432 · Cited by 244 · see citing works
Yasmin, F., Shah, S. M. I., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., Hassan, S. A., et al. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2204121 · Cited by 171 · see citing works
Artificial intelligence in cardiovascular medicine: clinical applications
Lüscher, T. F., Wenzl, F. A., D’Ascenzo, F., Friedman, P. A., & Antoniades, C. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae465 · Cited by 160 · see citing works
Li, X., Zhao, Y., Zhang, D., Kuang, L., Huang, H., Chen, W., Fu, X., Wu, Y., Li, T.-Z., Zhang, J., et al. (2022) · Chemosphere · doi:10.1016/j.chemosphere.2022.137039 · Cited by 130 · see citing works
Atualização de Tópicos Emergentes da Diretriz Brasileira de Insuficiência Cardíaca – 2021
Marcondes‐Braga, F. G., Moura, L. Z., Issa, V. S., Vieira, J. L., Rohde, L. E. P., Simões, M. V., Fernandes-Silva, M. M., Rassi, S., Martins, S. M., de Albuquerque, D. C., et al. (2021) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20210367 · Cited by 159 · see citing works
ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure
Akbilgiç, O., Butler, L., Karabayir, İ., Chang, P. P., Kitzman, D. W., Alonso, Á., Chen, L. Y., & Soliman, E. Z. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab080 · Cited by 115 · see citing works
Applications of artificial intelligence and machine learning in heart failure
Averbuch, T., Sullivan, K., Sauer, A. J., Mamas, M. A., Voors, A. A., Gale, C. P., Metra, M., Ravindra, N. G., & Spall, H. G. C. V. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac025 · Cited by 104 · see citing works
Dingel, J., Kleine, A., Cecil, J., Sigl, A., Lermer, E., & Gaube, S. (2024) · Journal of Medical Internet Research · doi:10.2196/57224 · Cited by 104 · see citing works
Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning
Kainz, B., Heinrich, M. P., Makropoulos, A., Oppenheimer, J., Mandegaran, R., Sankar, S., Deane, C., Mischkewitz, S., Al-Noor, F., Rawdin, A. C., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00503-7 · Cited by 86 · see citing works
Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance
Martini, N., Aimo, A., Barison, A., Latta, D. D., Vergaro, G., Aquaro, G. D., Ripoli, A., Emdin, M., & Chiappino, D. (2020) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-020-00690-4 · Cited by 85 · see citing works
Guo, A., Pasque, M. K., Loh, F. E., Mann, D. L., & Payne, P. (2020) · Current Epidemiology Reports · doi:10.1007/s40471-020-00259-w · Cited by 72 · see citing works
Role of artificial intelligence in early diagnosis and treatment of infectious diseases
Srivastava, V., Kumar, R., Wani, M. Y., Robinson, K. M., & Ahmad, A. (2024) · Scandinavian Journal of Infectious Diseases · doi:10.1080/23744235.2024.2425712 · Cited by 61 · see citing works
Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review
Almansouri, N. E., Awe, M., Rajavelu, S., Jahnavi, K., Shastry, R., Hasan, A., Hasan, H., Lakkimsetti, M., AlAbbasi, R. K., Criollo-Gutiérrez, B. D., et al. (2024) · Cureus · doi:10.7759/cureus.55869 · Cited by 69 · see citing works
Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future
Yoon, M., Park, J. J., Hur, T., Hua, C.-H., Hussain, M., Lee, S., & Choi, D. (2023) · International Journal of Heart Failure · doi:10.36628/ijhf.2023.0050 · Cited by 63 · see citing works
Self-citationArtificial Intelligence in Echocardiography: The Time is Now
Sehly, A. A., Jaltotage, B., He, A., Maiorana, A., Ihdayhid, A. R., Rajwani, A., & Dwivedi, G. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2308256 · Cited by 45 · see citing works
Current status of heart failure: global and Korea
Park, J. J., & Choi, D. (2020) · The Korean Journal of Internal Medicine · doi:10.3904/kjim.2020.120 · Cited by 43 · see citing works
Self-citationZou, L., Huang, Z., Yu, X., Zheng, J., Liu, A., & Lei, M. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3227955 · Cited by 41 · see citing works
Mizna, S., Arora, S., Saluja, P., Das, G., & Alanesi, W. A. (2025) · European journal of medical research · doi:10.1186/s40001-025-02603-6 · Cited by 48 · see citing works
Adopting artificial intelligence in cardiovascular medicine: a scoping review
Makimoto, H., & Kohro, T. (2023) · Hypertension Research · doi:10.1038/s41440-023-01469-7 · Cited by 36 · see citing works
Artificial intelligence in cardiology: The past, present and future
Gupta, M. D., Kunal, S., Girish, M. P., Gupta, A., & Yadav, R. K. (2022) · Indian Heart Journal · doi:10.1016/j.ihj.2022.07.004 · Cited by 40 · see citing works
Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool
Suresh, V., Singh, K. K., Vaish, E., Gurjar, M., Anubuvanan, A. M., Khulbe, Y., & Muzaffar, S. N. (2024) · Cureus · doi:10.7759/cureus.59797 · Cited by 36 · see citing works
Data-driven decision making in patient management: a systematic review
Lyu, G. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03072-x · Cited by 32 · see citing works
Digital tools in heart failure: addressing unmet needs
Myhre, P. L., Tromp, J., Ouwerkerk, W., Ting, D. S. W., Docherty, K. F., Gibson, C. M., & Lam, C. S. P. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00158-4 · Cited by 31 · see citing works
Machine Learning Augmented Echocardiography for Diastolic Function Assessment
Fletcher, A. J., Lapidaire, W., & Leeson, P. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.711611 · Cited by 29 · see citing works
Recommendations for Successful Development and Implementation of Digital Health Technology Tools
Loo, R. T. J., Nasta, F., Macchi, M., Baudot, A., Burstein, F., Bove, R. M., Greve, M., Fröhlich, H., Khalid, S., Küderle, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/56747 · Cited by 28 · see citing works
Wang, A., Xiu, X., Liu, S., Qian, Q., & Wu, S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192013691 · Cited by 30 · see citing works
Artificial Intelligence in Diagnosis of Heart Failure
Xie, Y., Zhang, L., Sun, W., Zhu, Y. C., Zhang, Z., Chen, L., Xie, M., & Zhang, L. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.039511 · Cited by 27 · see citing works
Echocardiographic Advances in Dilated Cardiomyopathy
Faggiano, A., Avallone, C., Gentile, D., Provenzale, G., Toriello, F., Merlo, M., Sinagra, G., & Carugo, S. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10235518 · Cited by 26 · see citing works
Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic
Gopinath, N. (2021) · Process Biochemistry · doi:10.1016/j.procbio.2021.08.001 · Cited by 24 · see citing works
Gruca, M. M., Slivnick, J. A., Singh, A., Cotella, J. I., Subashchandran, V., Prabhu, D. S., Asch, F. M., Siddiki, M., Gupta, N., Mor‐Avi, V., et al. (2023) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jead231 · Cited by 23 · see citing works
Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review
Medhi, D., Kamidi, S. R., Sree, K. P. M., Shaikh, S., Rasheed, S., Murichathil, A. H. T., & Nazir, Z. (2024) · Cureus · doi:10.7759/cureus.59661 · Cited by 25 · see citing works
Neues zur Diagnostik und Therapie der Herzinsuffizienz
Wintrich, J., Berger, A.-K., Bewarder, Y., Emrich, I. E., Slawik, J., & Böhm, M. (2021) · Herz · doi:10.1007/s00059-021-05062-x · Cited by 23 · see citing works
Sandeep, B., Liu, X., Huang, X., Wang, X., Mao, L., & Xiao, Z. (2023) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2023.102349 · Cited by 21 · see citing works
Heart Failure Nurses within the Primary Care Setting
Hill, L., McNulty, A., McMahon, J., Mitchell, G., Farrell, C., Uchmanowicz, I., & Castiello, T. (2024) · Cardiac failure review · doi:10.15420/cfr.2023.15 · Cited by 21 · see citing works
Clinical decision support system and hospital readmission reduction: Evidence from U.S. panel data
Park, Y., Bang, Y., & Kwon, J. (2022) · Decision Support Systems · doi:10.1016/j.dss.2022.113816 · Cited by 20 · see citing works
Virtual healthcare solutions in heart failure: a literature review
Lee, K. C.-S., Breznen, B., Ukhova, A., Martin, S. S., & Koehler, F. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1231000 · Cited by 18 · see citing works
Building trust in artificial intelligence and new technologies in mental health
O’Dell, B., Stevens, K., Tomlinson, A., Singh, I., & Cipriani, A. (2022) · Evidence-Based Mental Health · doi:10.1136/ebmental-2022-300489 · Cited by 20 · see citing works
Sunjaya, A. P., Ansari, S., & Jenkins, C. R. (2022) · npj Primary Care Respiratory Medicine · doi:10.1038/s41533-022-00291-x · Cited by 19 · see citing works
Zippel‐Schultz, B., Palant, A., Eurlings, C., Ski, C. F., Hill, L., Thompson, D. R., Fitzsimons, D., Dixon, L., Brandts, J., Schuett, K. A., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-046160 · Cited by 19 · see citing works
VIRDOCD : A VIRtual DOCtor to predict dengue fatality
Chattopadhyay, A. K., & Chattopadhyay, S. (2021) · Expert Systems · doi:10.1111/exsy.12796 · Cited by 18 · see citing works
Huang, Z., Lim, H. Y.-F., Ow, J. T., Sun, S. H.-L., & Chow, A. L. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1420032 · Cited by 19 · see citing works
Georgopoulou, T., Petrakis, I., Dermitzaki, K. E., Pleros, C., Drosataki, E., Aletras, G., Foukarakis, E. G., Lioudaki, E., Androulakis, E. S., & Stylianou, K. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12124121 · Cited by 18 · see citing works
Zhang, H., & Mu, R. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132506 · Cited by 16 · see citing works
Xiao, H., Liang, X., Li, H., Chen, X., & Li, Y. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117238 · Cited by 15 · see citing works
Shin, H. J., Cho, I. T., Choi, W., Kim, H. R., Kang, M.-B., & Yang, W. (2024) · Journal of Yeungnam Medical Science · doi:10.12701/jyms.2024.01004 · Cited by 15 · see citing works
Han, Q., Dong, B., Yuan, J., Yin, F., Wang, Z., Wang, H.-N., Wang, H.-S., Tian, D., Li, W.-H., Zhang, B., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.695185 · Cited by 15 · see citing works
Role of Imaging and AI in the Evaluation of COVID-19 Infection: A Comprehensive Survey
Elgendy, M., Balaha, H. M., Shehata, M., Alksas, A., Ghoneim, M. M. R., Sherif, F., Mahmoud, A. H., Elgarayhi, A., Taher, F., Sallah, M., et al. (2022) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2709276 · Cited by 15 · see citing works
Najim, A. H., & Nasri, N. (2024) · Cogent Engineering · doi:10.1080/23311916.2024.2325635 · Cited by 14 · see citing works
Jiao, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17613-x · Cited by 13 · see citing works
Xu, S., & Sun, M. (2024) · Journal of Clinical Hypertension · doi:10.1111/jch.14768 · Cited by 13 · see citing works
Heart failure monitoring with a single‑lead electrocardiogram at home
Hasumi, E., Fujiu, K., Chen, Y., Miyamoto, S., Oida, M., Shimizu, Y., Kani, K., Goto, K., Uchida, R., Liu, Y., et al. (2025) · International Journal of Cardiology · doi:10.1016/j.ijcard.2025.133203 · Cited by 12 · see citing works
Pană, M.-A., Busnatu, Ș., Șerbănoiu, L. I., Vasilescu, E., Popescu, N., Andrei, C. L., & Sinescu, C. J. (2021) · Applied Sciences · doi:10.3390/app112411728 · Cited by 11 · see citing works
Diagnostic Strategies Using AI and ML in Cardiovascular Diseases: Challenges and Future Perspectives
Rana, N., Sharma, K., & Sharma, A. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-1285-7_7 · Cited by 10 · see citing works
Choi, D., Park, J. J., Yoon, M., Park, S., Jo, S., Kim, E. J., Kim, S., & Lee, S. (2022) · Korean Circulation Journal · doi:10.4070/kcj.2022.0133 · Cited by 10 · see citing works
Self-citationKavas, P. Ö., Bozkurt, M. R., Kocayiğit, İ., & Bílgín, C. (2022) · IEEE Access · doi:10.1109/access.2022.3213065 · Cited by 8 · see citing works
Charman, S. J., Okwose, N. C., Groenewegen, A., Franco, A. D., Tafelmeier, M., Preveden, A., Sebastián, C. G., Fuller, A. S., Sinclair, D. R., Edwards, D., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-091793 · Cited by 8 · see citing works
Avances en insuficiencia cardiaca
Zamorano, J. L., & Leal, A. G. (2024) · Medicina Clínica · doi:10.1016/j.medcli.2023.12.026 · Cited by 8 · see citing works
Xu, S., & Sun, M. (2024) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-024-00666-w · Cited by 8 · see citing works
Majd, F. K., Gavgani, V. Z., Golmohammadi, A., & Jafari-Khounigh, A. (2021) · Health Informatics Journal · doi:10.1177/1460458221996409 · Cited by 8 · see citing works
Ali, S. I., Jung, S. W., Bilal, H. S. M., Lee, S. H., Hussain, J., Afzal, M., Hussain, M., Ali, T., Chung, T., & Lee, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19010226 · Cited by 9 · see citing works
Self-citationHenkin, S., Houghton, D. E., Hunsaker, A. R., Zuin, M., Pfeferman, M. B., Sato, A., & Piazza, G. (2025) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2025.05.025 · Cited by 8 · see citing works
Biomedical Applications of Bioresorbable Mechanical Sensors
Wu, Y., Liu, Z., & Fan, Y. (2025) · Advanced Functional Materials · doi:10.1002/adfm.202513422 · Cited by 7 · see citing works
Zhang, C. H., Yang, Z., & Tang, Y.-D. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004000 · Cited by 8 · see citing works
Zhao, B., Gao, Z., Liu, X., Zhang, Z., Xiao, W., & Zhang, S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107680 · Cited by 7 · see citing works
Agarwal, A., Thirunarayan, K., Romine, W. L., Alambo, A., Cajita, M., & Banerjee, T. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871400 · Cited by 7 · see citing works
Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors
Yen, C., Epling, J. W., Rockwell, M., & Vaughn-Cooke, M. (2026) · Diagnostics · doi:10.3390/diagnostics16020347 · Cited by 6 · see citing works
Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation
Sabir, S., Maqbool, F., Razzaq, S., Shah, D., Ali, S., Tahir, M., & Shehzad, H. M. F. (2025) · BMC Cardiovascular Disorders · doi:10.1186/s12872-025-04956-6 · Cited by 6 · see citing works
Garza-Frias, E., Kaviani, P., Karout, L., Fahimi, R., Hosseini, S., Putha, P., Tadepalli, M., Kiran, S., Arora, C., Robert, D., et al. (2024) · Diagnostics · doi:10.3390/diagnostics14151635 · Cited by 8 · see citing works
Xu, S., & Sun, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-95938-3 · Cited by 6 · see citing works
Almusharraf, A. I., Shaikh, A. K., Attar, R. W., & Alwassil, O. I. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1550671 · Cited by 7 · see citing works
Hussain, M., Afzal, M., Malik, K. M., Ali, T., Khan, W. A., Irfan, M., Jamshed, A., & Lee, S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105701 · Cited by 6 · see citing works
Self-citationDetecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review
Liastuti, L. D., Siswanto, B. B., Sukmawan, R., Jatmiko, W., Nursakina, Y., Putri, R. Y. I., Jati, G., & Nur, A. A. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2312402 · Cited by 8 · see citing works
Freitas, A. F., Silveira, F. S., Conceição‐Souza, G. E., Canesin, M. F., Schwartzmann, P. V., Bernardez‐Pereira, S., & Bestetti, R. B. (2020) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20201127 · Cited by 6 · see citing works
Pană, M.-A., Busnatu, Ș., Șerbănoiu, L. I., Vasilescu, E., Popescu, N., Andrei, C. L., & Sinescu, C. J. (2021) · Vide Leaf, Hyderabad eBooks · doi:10.37247/paasc.1.22.13 · Cited by 6 · see citing works
Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?
Jankauskas, S. S., Varzideh, F., Kansakar, U., & Santulli, G. (2026) · Journal of Personalized Medicine · doi:10.3390/jpm16040192 · Cited by 5 · see citing works
Trust in Artificial Intelligence: Clinicians Are Essential
Bhatt, U., & Shams, Z. (2021) · Health informatics · doi:10.1007/978-3-030-81030-6_10 · Cited by 5 · see citing works
Heart Disease Prediction Using Hybrid Random Forest Model Integrated with Linear Model
Wankhede, J. P., Sambandam, P., & Kumar, S. M. (2021) · Advances in parallel computing · doi:10.3233/apc210160 · Cited by 5 · see citing works
The Impact of Artificial Intelligence on Women’s Cardiovascular Disease Care
Chung, J., Thangathurai, J., Andrade-Bucknor, S. N., Parashar, S., Carta, K. G., Epp, M., & Ma, C. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02250-7 · Cited by 5 · see citing works
Gnanadurai, G. J., Raaza, A., Velayutham, R., Palani, S. K., & Ebenezer Abishek, B. (2022) · Computational Intelligence · doi:10.1111/coin.12505 · Cited by 5 · see citing works
Lee, R. S., Ong, J., Waisberg, E., Ben-David, G. S., Jaiswal, S., Arogundade, E. O., Tavakkoli, A., & Lee, A. G. (2025) · Neuro-Ophthalmology · doi:10.1080/01658107.2025.2459206 · Cited by 6 · see citing works
Great debate: artificial intelligence will replace much of what cardiologists do
Averbuch, T., Asselbergs, F. W., Vardas, P. E., & Spall, H. G. C. V. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehaf305 · Cited by 5 · see citing works
James, N., Gerrish, L., Rokotyan, N., & Gladding, P. A. (2021) · medRxiv · doi:10.1101/2021.07.26.21261115 · Cited by 5 · see citing works
Heart Failure in Sub-Saharan Africa: Current and Future Systems of Care
Wauye, V. M., Ahadzi, D., Udayakumar, K., & Ngeno, G. T. K. (2025) · Journal of Asian Pacific Society of Cardiology · doi:10.15420/japsc.2024.37 · Cited by 4 · see citing works
The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial
Frydman, S., Freund, O., Miller, R., Sror, N., Barel, N., Baruch, G., Rothschild, E., Merin, R., Shporn, O. Z., Ohad, M., et al. (2026) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztag013 · Cited by 4 · see citing works
Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research
Gouda, P., & Ezekowitz, J. A. (2022) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-022-10313-1 · Cited by 4 · see citing works
A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure
Coiro, S., Lacomblez, C., Duarte, K., Gargani, L., Rastogi, T., Chouihed, T., & Girerd, N. (2024) · Internal and Emergency Medicine · doi:10.1007/s11739-024-03627-2 · Cited by 4 · see citing works
Diagnostic Modalities in Heart Failure: A Narrative Review
Tanisha, Amudha, C., Raake, M., Samuel, D., Aggarwal, S., Bashir, Z. M. D., Marole, K. K., Maryam, I., & Nazir, Z. (2024) · Cureus · doi:10.7759/cureus.67432 · Cited by 4 · see citing works
Jang, J.-H., Lee, S., Kim, D., Shin, S., Lee, S., Kim, D., Choi, W., & Baek, Y. (2024) · Open Heart · doi:10.1136/openhrt-2024-002924 · Cited by 4 · see citing works
Self-citationDiversity and Inclusion Within Datasets in Heart Failure
Laws, E., Charalambides, M., Vadera, S., Keller, E., Alderman, J. E., Blackboro, B., Hogg, H. D. J., Salisbury, T., Palmer, J., Calvert, M., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101610 · Cited by 4 · see citing works
Kulkarni, K., Isselbacher, E. M., & Armoundas, A. A. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch12 · Cited by 4 · see citing works
AI-Driven Rehabilitation: Personalizing Cardiac Care Post-Diagnosis
Kosaraju, D. (2024) · Galore International Journal of Health Sciences and Research · doi:10.52403/gijhsr.20240121 · Cited by 3 · see citing works
From the Iron Lung to Artificial Intelligence: Integrating New Technology Into Critical Care
Lee, S. Y., & Jeffery, A. D. (2026) · American Journal of Critical Care · doi:10.4037/ajcc2026154 · Cited by 3 · see citing works
Self-citationHeart Physiology and Heart Disease
Okwose, N. C., Charman, S. J., Fuller, A. S., MacGowan, G., Filipović, N., & Jakovljević, D. G. (2024) · Springer eBooks · doi:10.1007/978-3-031-60044-9_3 · Cited by 3 · see citing works
Artificial intelligence in cardiovascular practice
Farquhar-Snow, M., Simone, A. E., Singh, S. V., & Bushardt, R. L. (2025) · JAAPA · doi:10.1097/01.jaa.0000000000000204 · Cited by 4 · see citing works
Li, X., Liao, J.-P., Chen, M.-K., Gao, K., Wang, Y., Yan, S., Huang, Q. Y., Wang, X., Shi, Y., Hu, W., et al. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-02007-1 · Cited by 3 · see citing works
Júnior, W. B. D. S., Filho, M. M. P., Barreto, S. M., Foppa, M., Giatti, L., Khera, R., & Ribeiro, A. L. P. (2025) · Arquivos Brasileiros de Cardiologia · doi:10.36660/abc.20240740i · Cited by 3 · see citing works
Afanasieva, T. V., Perfilieva, I., & Kozhevnikov, V. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-87178-9_46 · Cited by 3 · see citing works
Interpreting Diastolic Dynamics and Evaluation through Echocardiography
Zhang, X., Li, K., Cardoso, C. D. O., Moctezuma-Ramirez, A., & Elgalad, A. (2024) · Life · doi:10.3390/life14091156 · Cited by 2 · see citing works
Brar, S. S., Yathindra, M. R., Arango, J. S. A., Gutierrez, E. A., Aldoohan, F., Chahal, P. S., Youssef, A., Narra, M. S., Tatineni, M. B., Saldaña, J. M. A., et al. (2025) · Current Cardiology Reviews · doi:10.2174/011573403x369978250818060357 · Cited by 2 · see citing works
Integrating Machine Learning in Clinical Decision Support for Heart Failure Diagnosis: Case Study
Spahić, L., Softić, A., Durak-Nalbantić, A., Begić, E., Stanetić, B., & Vranić, H. (2024) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-49062-0_73 · Cited by 2 · see citing works
- Authors
- Choi, D.-J., Park, J. J., Ali, T., & Lee, S.
- Affiliations
- Seoul National University (KR)
- Kyung Hee University (KR)
- Corresponding author
- Choi, Dong-Ju
- Lee, Sungyoung
- Venue
- npj Digital Medicine
- Published
- 2020-04-08
- Volume
- 3
- Issue
- 1
- Pages
- 54-54
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7142093
- OpenAlex
- W3015932015
- Cited by
- 163
- References
- 22
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 39 · 2023: 23 · 2022: 21 · 2021: 21 · 2020: 6
- FWCI
- 15.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Iran Telecommunication Research Center (IITP-2017-0-01629)
- Ministry of Science and ICT, South Korea (IITP-2017-0-01629)
- Institute for Information and Communications Technology Promotion (IITP-2017-0-01629)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Failure Treatment and Management
- Keywords
- heart failure diagnosis, artificial intelligence, clinical decision support systems, diagnostic accuracy, dyspnea, heart failure with preserved ejection fraction, heart failure with reduced ejection fraction, mid-range ejection fraction
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