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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 [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 20,604
Lang, R. M., Badano, L. P., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F. A., Foster, E., Goldstein, S. A., Kuznetsova, T., et al. (2015) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2014.10.003 · Cited by 12,362
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
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
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
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 1,165
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 1,064
Reasoning Foundations of Medical Diagnosis
Ledley, R. S., & Lusted, L. B. (1959) · Science · doi:10.1126/science.130.3366.9 · Cited by 998
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 944
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
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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 135
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Dingel, J., Kleine, A., Cecil, J., Sigl, A., Lermer, E., & Gaube, S. (2024) · Journal of Medical Internet Research · doi:10.2196/57224 · Cited by 116
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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 73
Role of artificial intelligence in early diagnosis and treatment of infectious diseases
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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 53
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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 37
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Lyu, G. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03072-x · Cited by 32
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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 22
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Sunjaya, A. P., Ansari, S., & Jenkins, C. R. (2022) · npj Primary Care Respiratory Medicine · doi:10.1038/s41533-022-00291-x · Cited by 19
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 20
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Chattopadhyay, A. K., & Chattopadhyay, S. (2021) · Expert Systems · doi:10.1111/exsy.12796 · Cited by 17
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
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Zhang, H., & Mu, R. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132506 · Cited by 16
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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
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Xu, S., & Sun, M. (2024) · Journal of Clinical Hypertension · doi:10.1111/jch.14768 · Cited by 13
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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 12
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Rana, N., Sharma, K., & Sharma, A. (2025) · Algorithms for intelligent systems · doi:10.1007/978-981-96-1285-7_7 · Cited by 11
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 11
Self-citationKavas, P. Ö., Bozkurt, M. R., Kocayiğit, İ., & Bílgín, C. (2022) · IEEE Access · doi:10.1109/access.2022.3213065 · Cited by 9
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
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Xu, S., & Sun, M. (2024) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-024-00666-w · Cited by 8
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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 9
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Zhang, C. H., Yang, Z., & Tang, Y.-D. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004000 · Cited by 9
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
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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
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
Xu, S., & Sun, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-95938-3 · Cited by 6
Almusharraf, A. I., Shaikh, A. K., Attar, R. W., & Alwassil, O. I. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1550671 · Cited by 8
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
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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
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
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
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James, N., Gerrish, L., Rokotyan, N., & Gladding, P. A. (2021) · medRxiv · doi:10.1101/2021.07.26.21261115 · Cited by 5
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Kulkarni, K., Isselbacher, E. M., & Armoundas, A. A. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch12 · Cited by 4
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Metadata
- 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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