The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Cited by
Sorted by citations
Items 101–163 of 163
Mesran, M., Kmurawak, R. M., & Windarto, A. P. (2023) · TELKOMNIKA (Telecommunication Computing Electronics and Control) · doi:10.12928/telkomnika.v22i1.25357 · Cited by 2
AVANÇOS DA INTELIGÊNCIA ARTIFICIAL NA CARDIOLOGIA: UMA REVISÃO ABRANGENTE
Martins, V. B. L., Thom, L. R. F., Gonçalves, J. M. S., & Destêfani, A. C. (2024) · Revista Ibero-Americana de Humanidades, Ciências e Educação · doi:10.51891/rease.v10i4.13643 · Cited by 2
Advances in heart failure management
Zamorano, J. L., & Leal, A. G. (2024) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2024.06.001 · Cited by 2
Lang, F. M., Lee, B. C., Lotan, D., Sabuncu, M. R., & Topkara, V. K. (2024) · Methodist DeBakey Cardiovascular Journal · doi:10.14797/mdcvj.1392 · Cited by 2
Ju, C.-Y., & Lee, D. (2024) · Methods · doi:10.1016/j.ymeth.2024.09.012 · Cited by 2
Hamad, A. K., & Haji, J. (2025) · Current Cardiology Reviews · doi:10.2174/011573403x334095241205041550 · Cited by 3
Heyman, E. T., Ashfaq, A., Ekelund, U. E. G., Ohlsson, M., Björk, J., Schubert, A. M., Lingman, M., & Khoshnood, A. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105969 · Cited by 2
Neural network models selection scheme for health mobile app development
Triana, Y. S., Osman, M. A., Pratomo, A., Pasha, M. F., Stiawan, D., & Budiarto, R. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i3.pp1191-1203 · Cited by 5
A Statistical Study on Intelligent Systems for the Diagnosis of Heart Failure
Singh, J., & Singh, D. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10397887 · Cited by 2
Machine Learning in Heart Failure Research: A Bibliometric Analysis from 2003 to 2023Preprint
Yu, D., Yang, S., Wang, R., Wang, K., Han, W., Wu, H., Wang, W., & Wang, X. (2023) · Research Square · doi:10.21203/rs.3.rs-3024872/v1 · Cited by 3
Zhou, J., Murugappan, L., Lü, L., Chou, O. H. I., Cheung, B. M. Y., Tse, G., & Zhu, T. (2023) · medRxiv · doi:10.1101/2023.11.15.23298562 · Cited by 2
Interpreting Diastolic Dynamics and Evaluation Through EchocardiographyPreprint
Zhang, X., Li, K., Cardoso, C. D. O., Moctezuma-Ramirez, A., & Elgalad, A. (2024) · Preprints.org · doi:10.20944/preprints202403.0952.v1 · Cited by 1
Interpreting Diastolic Dynamics and Evaluation Through EchocardiographyPreprint
Zhang, X., Li, K., Cardoso, C. D. O., Moctezuma-Ramirez, A., & Elgalad, A. (2024) · Preprints.org · doi:10.20944/preprints202403.0952.v2 · Cited by 1
Abhishek, A., Bhatt, P., Baig, M. N., Sridevi, R., & Hazela, B. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-3212-2.ch007 · Cited by 3
Harnessing machine learning technique for improved detection and classification of heart failure
Rao, P. S., & Rachapudi, V. (2024) · Data & Metadata · doi:10.56294/dm2024.356 · Cited by 1
Okumura, T., Kusunose, K., Tsuji, T., Kotoku, J., Todaka, K., & Saku, K. (2025) · Cardiology Discovery · doi:10.1097/cd9.0000000000000168 · Cited by 1
Duplyakov, D. V., Shebonkina, D. A., Uranov, A. E., Mikheenko, I., Benimetskaya, K. S., А., Г. А., Вячеславовна, А. Е., Tukhbatova, A. A., Kuzmin, V. P., Fedorova, G. A., et al. (2026) · Russian Journal of Cardiology · doi:10.15829/1560-4071-2025-6365 · Cited by 1
AI technologies for smart pharmacology
Vișan, A. I., & Neguț, I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00003-8 · Cited by 1
Artificial intelligence in heart failure
Li, X., Liu, Y., Zhang, X., Yang, N., Xu, T. W., Cui, X. W., & Chen, G. (2026) · The Egyptian Heart Journal · doi:10.1186/s43044-026-00723-w · Cited by 1
Kumar, P., Alnaimi, N. A. A., Soman, S., Suansing, L., Arriola, D. R., & Jamea, L. A. (2026) · Sci · doi:10.3390/sci8040073 · Cited by 2
End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT
Tang, F.-S. K.-B., Verket, M., Müller‐Wieland, D., Brandts, J., Jacobsen, M., Pütz, A., Marx-Schütt, K., Marx, N., Schmitz‐Rode, T., & Slabu, I. (2026) · Scientific Reports · doi:10.1038/s41598-026-48771-1 · Cited by 1
Artificial Intelligence in Heart Failure with Preserved Ejection Fraction
Li, X., Xu, C., Deng, W., Zhang, Y., Liu, C. X., Gao, L. L., Ji, M., He, Q., Wu, Z., Qin, S., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16111597 · Cited by 1
Recent Advancements in the Management of Heart Failure: A Review
Sanyaolu, S. E. (2025) · Premier Journal of Cardiology · doi:10.70389/pjc.100008 · Cited by 1
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.20240740 · Cited by 4
MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction
AbdElminaam, D. S., Madbouly, M. M., Zeid, M. A.-E., Gomaa, I. A. E., & Abualigah, L. (2023) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc58832.2023.10278372 · Cited by 1
Machine Learning Techniques for Predicting and Managing Heart Failure
Plati, D. K., Tripoliti, E. E., Karanasiou, G. S., Rammos, A., Bechlioulis, A., Watson, C. J., McDonald, K., Ledwidge, M. T., Goletsis, Y., Naka, K. Κ., et al. (2022) · Wiley eBooks · doi:10.1002/9781119813040.ch9 · Cited by 1
van Rooij, T., Ali, T., Toledo-Marín, J. Q., Laws, A., Barrett, B., Payne, B. A., Amed, S., Portales-Casamar, É., Wasserman, W. W., & Görges, M. (2024) · doi:10.2196/preprints.70061 · Cited by 1
Self-citationVIRDOC: Statistical and Machine Learning by a VIRtual DOCtor to Predict Dengue Fatality.Preprint
Chattopadhyay, A. K., & Chattopadhyay, S. (2021) · arXiv (Cornell University)
Potential Role of Artificial Intelligence for the Previous Study Using Traditional Analysis
Nakajima, K., Igata, M., & Higuchi, R. (2021) · Journal of Clinical Medicine Research · doi:10.14740/jocmr4568
Remote monitoring of single-lead electrocardiography enables detection of heart failure statusPreprint
Hasumi, E., Fujiu, K., Chen, Y., Oida, M., Shimizu, Y., Kani, K., Goto, K., Uchida, R., Liu, Y., Oshima, T., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1308790/v1
Automated algorithms in diastology: how to move forward?
Strachinaru, M., & Bosch, J. G. (2022) · The International Journal of Cardiovascular Imaging · doi:10.1007/s10554-021-02505-8
Tilgang til helseopplysninger i maskinlæringsprosjekter
Hauglid, M. K., & Mikalsen, K. Ø. (2022) · Lov og Rett · doi:10.18261/lor.61.7.3
Clinical applications of machine learning in heart failure
Li, X., Lee, S., Bazoukis, G., Tse, G., & Liu, T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-819872-8.00017-3
Self-citationYao, L., Wang, J., Zhang, C., Wu, Z., Luo, C., Zhang, L., Xiao, B., Gong, R., Dong, Z., Li, H., et al. (2023) · doi:10.2196/preprints.48089
Application of Artificial Intelligence in Three-level Prevention of Heart Failure (Preprint)Preprint
Ma, H., Chu, Q., Ruichen, C., Wu, L., Conghua, J., & Mao, W. (2023) · doi:10.2196/preprints.50634
Hybrid Algorithm to Predict Heart Disease- A Survey
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets43730
Artificial intelligence in heart failure and transplant
Sarnaik, K. S., & Tang, W. H. W. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00036-x
Nugraha, K. A., Rohman, M. S., Lukitasari, M., Rahimah, A. F., Anjarwani, S., Rizal, A., Astiawati, T., Adi, A. W., & Haryati, L. (2023) · Heart Science Journal · doi:10.21776/ub.hsj.2023.004.04.5
IoT-Enabled Fuzzy Inference System for Heart Disease Monitoring
Singh, J., & Singh, D. (2023) · Transactions on computer systems and networks · doi:10.1007/978-981-99-4518-4_8
Gozali, E., Hajesmaeel‐Gohari, S., Khademvatani, K., & Asr, R. T. (2024) · Frontiers in Health Informatics · doi:10.30699/fhi.v13i0.541
Artificial intelligence in cardiovascular imaging: advances and challenges
Alkhodari, M., Moussa, M. M., & Dhou, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95462-4.00009-1
Diagnostic Deep Learning Framework for Heart FailurePreprint
Chanprasertpinyo, W., Phongkitkarun, S., Sriprachya, A., Nitiwarangkul, C., Thammasudjarit, R., Lolak, S., & Yingchoncharoen, T. (2024) · Research Square · doi:10.21203/rs.3.rs-4005522/v1
IDT-Editor: A Web Application for Streamlining Expert Knowledge Acquisition
Hussain, M., Satti, F. A., Ali, T., Choi, D., & Lee, S. (2024) · International Conference on Platform Technology and Service · doi:10.1109/platcon63925.2024.10830724
Self-citationParhi, D. K., Varma, P., & Awasthi, S. (2023) · Seminars in Medical Writing and Education · doi:10.56294/mw2023126
The Role of Artificial Intelligence in Achieving Sustainable Development Goals (SDGs)
Clark, T. (2024) · Palgrave Studies in Sustainable Futures. · doi:10.1007/978-3-031-24952-5_1-1
Artificial intelligence in cardiovascular practice
Farquhar-Snow, M., Simone, A. E., Singh, S. V., & Bushardt, R. L. (2025) · The Nurse Practitioner · doi:10.1097/01.npr.0000000000000312
Zhang, H., Zhang, P., Ma, F., Chao, L., Shan, W., & Li, Q. (2024) · International Conference on Bioinformatics and Computational Biology (ICBCB) · doi:10.1109/icbcb61507.2024.11012075
Tsaga, I. (2025) · Forum Dienstleistungsmanagement · doi:10.1007/978-3-658-48325-8_18
The Role of Artificial Intelligence in Achieving Sustainable Development Goals (SDGs)
Clark, T., Zolnikov, T. R., Furio, F., Salinas, N. D., & Dick, S. (2025) · Palgrave Studies in Sustainable Futures. · doi:10.1007/978-3-031-24952-5_1-2
Milosavljević, I., Petrović, I., Ilić, A., Dabović, D., Milovančev, A., Remer, G., Vuković, V., Zdravković, D., Stefanović, M., Stojšić, S., et al. (2025) · ESC Heart Failure · doi:10.1002/ehf2.15397
Empowering Heart Failure Management Through Artificial Intelligence
Levra, A. G. (2025) · Springer eBooks · doi:10.1007/978-3-031-95256-2_10 · Cited by 1
Outpatient monitoring and self-care
Kayali, S. M., Alexander, J., Rawal, A., Alkhatib, D. A., & Jefferies, J. L. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-13279-7.00027-4
Kılıç, M. E., Arayıcı, M. E., Akbilgiç, O., & Yılmaz, M. B. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.12.022
Martini, N., Aimo, A., Barison, A., Latta, D. D., Vergaro, G., Aquaro, G. D., Ripoli, A., Emdin, M., & Chiappino, D. (2020) · Figshare · doi:10.6084/m9.figshare.13346967
Impact of Artificial Intelligence in Critical Care: A Narrative Review
Udupa, A. N., & Sandesh Kumar, K. J. (2025) · Journal of Acute Care · doi:10.5005/jp-journals-10089-0194
Zheng, Y., Qin, J., Lv, F., Li, X., & Guo, X. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2025.1694781
Alotaibi, A., & Sas, C. (2026) · JMIR Mental Health · doi:10.2196/91884
AI-Driven Heart Failure Decision Support in Skilled Nursing Facilities
Glajchen, E., Reed, M., Uemura, T., Contreras, J., Ozols, Z., Kini, A. S., & Bander, J. A. (2026) · JACC Case Reports · doi:10.1016/j.jaccas.2026.108561
Nona, P. J., Firos, J. R. B., Zografos, C., Schuuring, M. J., Bhatt, A., & Andrikopoulou, E. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00766-9
Mapping the research landscape of artificial intelligence in heart failure: a bibliometric analysis
Rashidian, P., Jala, S. R. A., Somu, K. P., Mahapatro, A., Chellapuram, N., Hashemi, S. M., Alotaibi, A. J., Hasan, O. H., Nasrollahizadeh, A., Letafatkar, N., et al. (2026) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000005235
Artificial Intelligence in Cardiology: Applications in Diagnosis and Risk Prediction
Anand Sekar, G., Kulkarni, A. V., Sharma, C. K., Tank, B. Y., Sharat Vishwanath, K., & Lakhani, H. A. (2026) · Cureus · doi:10.7759/cureus.112841
Analyzing the Effectiveness of Precision Instrument Identification and Detection Capability
Weng, T.-S., & Huang, Y.-T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32115-2_21
Structuring Clinical Heart Failure Data with Locally Deployed OpenSource LLMs
Tsakonas, G. N., Samaras, A., Kosvyra, A., Giannakoulas, G., & Chouvarda, I. (2026) · International Balkan Conference on Communications and Networking (BalkanCom) · doi:10.1109/balkancom71095.2026.11605036
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
