The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Integrating blockchain technology with artificial intelligence for cardiovascular medicine
Open accessOverview
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
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?
In the citing paper
“the integration of blockchain with AI could advance the goal of personalized cardiovascular medicine.”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Krittanawong et al. 2020
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
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., et al. (2018) · Oncotarget · doi:10.18632/oncotarget.22345 · Cited by 471
Deep learning for cardiovascular medicine: a practical primer
Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., Min, J. K., Tang, W. H. W., Halperin, J. L., & Narayan, S. M. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz056 · Cited by 452
Self-citationSpironolactone Metabolites in TOPCAT — New Insights into Regional Variation
de Denus, S., O’Meara, E., Desai, A. S., Claggett, B. L., Lewis, E. F., Leclair, G., Jutras, M., Lavoie, J., Solomon, S. D., Pitt, B., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmc1612601 · Cited by 275
Artificial intelligence for the electrocardiogram
Mincholé, A., & Rodríguez, B. (2018) · Nature Medicine · doi:10.1038/s41591-018-0306-1 · Cited by 150
Design Choices and Trade-Offs in Health Care Blockchain Implementations: Systematic Review
O’Donoghue, O., Vazirani, A. A., Brindley, D. A., & Meinert, E. (2019) · Journal of Medical Internet Research · doi:10.2196/12426 · Cited by 82
Possible Usages of Smart Contracts (Blockchain) in Healthcare and Why No One Is Using Them
Giordanengo, A. (2019) · Studies in health technology and informatics · doi:10.3233/shti190292 · Cited by 29
Machine learning in ‘big data’: handle with care
Loring, Z., Mehrotra, S., & Piccini, J. P. (2019) · EP Europace · doi:10.1093/europace/euz130 · Cited by 21
Cited by
Sorted by citations
Artificial intelligence-enhanced electrocardiography in cardiovascular disease management
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00503-2 · Cited by 914
COVID-19: potential effects on Chinese citizens’ lifestyle and travel
Wen, J., Kozak, M., Yang, S., & Liu, F. (2020) · Tourism Review · doi:10.1108/tr-03-2020-0110 · Cited by 823
A survey of blockchain consensus algorithms performance evaluation criteria
Bamakan, S. M. H., Motavali, A., & Bondarti, A. B. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113385 · Cited by 497
Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001201 · Cited by 347
Self-citationBlockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281
Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00445-9 · Cited by 248
Self-citationThe Metaverse: A New Challenge for the Healthcare System: A Scoping Review
Petrigna, L., & Musumeci, G. (2022) · Journal of Functional Morphology and Kinesiology · doi:10.3390/jfmk7030063 · Cited by 236
CardioVerse: The cardiovascular medicine in the era of Metaverse
Skalidis, I., Müller, O., & Fournier, S. (2022) · Trends in Cardiovascular Medicine · doi:10.1016/j.tcm.2022.05.004 · Cited by 190
Supriya, M., & Chattu, V. K. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5030041 · Cited by 138
The Convergence of Artificial Intelligence and Blockchain: The State of Play and the Road Ahead
Bhumichai, D., Smiliotopoulos, C., Benton, R., Kambourakis, G., & Damopoulos, D. (2024) · Information · doi:10.3390/info15050268 · Cited by 175
Polas, M. R. H., Jahanshahi, A. A., Kabir, A. I., Sohel‐Uz‐Zaman, A. S. M., Osman, A., & Karim, R. (2022) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc8030168 · Cited by 134
Wang, S., Ding, S., & Xiong, L. (2020) · JMIR mhealth and uhealth · doi:10.2196/19457 · Cited by 102
Murala, D. K., Panda, S. K., & Dash, S. P. (2023) · IEEE Access · doi:10.1109/access.2023.3340791 · Cited by 129
Applications of Blockchain in the Medical Field: Narrative Review
Xie, Y., Zhang, J., Wang, H., Liu, P., Liu, S., Huo, T., Duan, Y.-Y., Dong, Z., Lu, L., & Ye, Z. (2021) · Journal of Medical Internet Research · doi:10.2196/28613 · Cited by 102
Blockchain, artificial intelligence, and healthcare: the tripod of future—a narrative review
Bathula, A., Gupta, S. K., Merugu, S., Saba, L., Khanna, N. N., Laird, J. R., Sanagala, S. S., Singh, R. K., Garg, D., Fouda, M. M., et al. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10873-5 · Cited by 110
Research on financing strategy under the integration of green supply chain and blockchain technology
Wu, C.-F., Xu, C., Zhao, Q., & Zhu, J.-H. (2023) · Computers & industrial engineering · doi:10.1016/j.cie.2023.109598 · Cited by 76
Kumar, R., Arjunaditya, Singh, D., Srinivasan, K., & Hu, Y. (2022) · Healthcare · doi:10.3390/healthcare11010081 · Cited by 92
Higher albumin levels on admission predict better prognosis in patients with confirmed COVID-19
Kheir, M. M., Saleem, F., Wang, C., Mann, A. S., & Chua, J. D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248358 · Cited by 81
Blockchain for Securing AI Applications and Open Innovations
Shinde, R., Patil, S., Kotecha, K., & Ruikar, K. (2021) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc7030189 · Cited by 75
Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
Łukaniszyn, M., Majka, Ł., Grochowicz, B., Mikołajewski, D., & Kawala‐Sterniuk, A. (2024) · Applied Sciences · doi:10.3390/app14209404 · Cited by 74
Krittanawong, C., Virk, H. U. H., Kumar, A., Aydar, M., Wang, Z., Stewart, M. P., & Halperin, J. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-88172-0 · Cited by 68
Self-citationHow will machine learning inform the clinical care of atrial fibrillation?
Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research · doi:10.1161/circresaha.120.316401 · Cited by 61
Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare
Zhu, Y., Li, J., Kim, J., Li, S., Zhao, Y., Bahari, J., Eliahoo, P., Li, G., Kawakita, S., Haghniaz, R., et al. (2023) · Biomaterials · doi:10.1016/j.biomaterials.2023.122075 · Cited by 65
Choi, Y., Ho, D. H., Kim, S., Choi, Y. J., Roe, D. G., Kwak, C. H., Min, J., Han, H., Gao, W. F., & Cho, J. H. (2023) · Science Advances · doi:10.1126/sciadv.adg5946 · Cited by 61
Qian, J.-P., Wu, W.-B., Yu, Q., Ruiz-García, L., Xiang, Y., Jiang, L., Shi, Y., Duan, Y., & Yang, P. (2020) · Food and Energy Security · doi:10.1002/fes3.249 · Cited by 45
Edge Computing to Secure IoT Data Ownership and Trade with the Ethereum Blockchain
Nawaz, A., Queralta, J. P., Guan, J., Awais, M. A., Gia, T. N., Bashir, A. K., Kan, H., & Westerlund, T. (2020) · Sensors · doi:10.3390/s20143965 · Cited by 59
Mikołajewska, E., Masiak, J., & Mikołajewski, D. (2024) · Electronics · doi:10.3390/electronics13244994 · Cited by 54
Decentralizing construction AI applications using blockchain technology
Adel, K., Elhakeem, A., & Marzouk, M. M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116548 · Cited by 60
Gupta, S., & Rhyner, J. (2022) · Sustainability · doi:10.3390/su14053114 · Cited by 62
Artificial Intelligence-Powered Blockchains for Cardiovascular Medicine
Krittanawong, C., Aydar, M., Virk, H. U. H., Kumar, A., Kaplin, S. L., Guimaraes, L., Wang, Z., & Halperin, J. L. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.11.011 · Cited by 53
Self-citationZhuo, Y., Yuan, R., Chen, X., He, J., Chen, Y., Zhang, C., Sun, K., Yang, S., Liu, Z., & Gao, H. (2021) · Chinese Medicine · doi:10.1186/s13020-021-00458-7 · Cited by 50
Can blockchain link the future?
Qiao, L., Dang, S., Shihada, B., Alouini, M., Nowak, R. M., & Lv, Z. (2021) · Digital Communications and Networks · doi:10.1016/j.dcan.2021.07.004 · Cited by 45
Artificial Intelligence for Web 3.0: A Comprehensive Survey
Shen, M., Tan, Z., Niyato, D. T., Liu, Y., Kang, J., Xiong, Z., Zhu, L., Wang, W., & Shen, X. (2024) · ACM Computing Surveys · doi:10.1145/3657284 · Cited by 49
A Traditional Chinese Medicine Traceability System Based on Lightweight Blockchain
Wang, Z., Wang, L., Xiao, F., Chen, Q., Lu, L., & Hong, J. (2021) · Journal of Medical Internet Research · doi:10.2196/25946 · Cited by 41
Self-citationArtificial intelligence and cardiology: Current status and perspective
Nakamura, T., & Sasano, T. (2021) · Journal of Cardiology · doi:10.1016/j.jjcc.2021.11.017 · Cited by 45
Recent advances in blockchain technology: a survey on applications and challenges
Hakak, S., Khan, W. Z., Gilkar, G. A., Assiri, B., Alazab, M., Bhattacharya, S., & Gadekallu, T. R. (2021) · International Journal of Ad Hoc and Ubiquitous Computing · doi:10.1504/ijahuc.2021.119089 · Cited by 47
Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence
Suri, J. S., Puvvula, A., Majhail, M., Biswas, M., Jamthikar, A. D., Saba, L., Faa, G., Singh, I. M., Oberleitner, R., Turk, M., et al. (2020) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm.2020.04.236 · Cited by 37
Arpaïa, P., Bracale, U. M., Corcione, F., Benedetto, E. D., Bernardo, A. D., Capua, V. D., Duraccio, L., Peltrini, R., & Prevete, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-16030-8 · Cited by 35
Anthony, B. (2024) · Cities · doi:10.1016/j.cities.2024.105198 · Cited by 33
Dynamics of Metaverse and Medicine: A Review Article
Kawarase, M. A., & Anjankar, A. P. (2022) · Cureus · doi:10.7759/cureus.31232 · Cited by 30
The TAO of Blockchain Intelligence for Intelligent Web 3.0
Li, J., & Wang, F. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.124056 · Cited by 29
Assessment of Blockchain Technology Application in the Improvement of Pharmaceutical Industry
Alshahrani, W. A., & Alshahrani, R. (2021) · International Conference of Women in Data Science at Taif University (WiDSTaif) · doi:10.1109/widstaif52235.2021.9430210 · Cited by 25
Zhan, L., Liu, Y., Cheng, Y., Guo, W., & Yang, J. (2021) · International Journal of General Medicine · doi:10.2147/ijgm.s317380 · Cited by 23
Rao, K. P., & Manvi, S. S. (2022) · Acta Informatica Pragensia · doi:10.18267/j.aip.194 · Cited by 32
Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?
Parsa, S., Somani, S. S., Dudum, R., Jain, S. S., & Rodríguez, F. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01210-w · Cited by 22
Current Status and Future Perspectives on the Internet of Things in Oncology
Muhsen, I. N., Rasheed, O. W., Habib, E., Alsaad, R., Maghrabi, M. K., Rahman, M. A., Sicker, D., Wood, W. A., Beg, M. S., Sung, A. D., et al. (2021) · Hematology/Oncology and Stem Cell Therapy · doi:10.1016/j.hemonc.2021.09.003 · Cited by 23
Lončarić, F., Cámara, Ó., Piella, G., & Bijnens, B. H. (2020) · Revista Española de Cardiología · doi:10.1016/j.recesp.2020.07.012 · Cited by 21
Skalidis, I., Müller, O., Fournier, S., Antiochos, P., Kaldasch, M., Idrissi, B. E., Briante, N., KOCHIADAKIS, G. E., Skalidis, E. I., & Maurizi, N. (2022) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2022.07.020 · Cited by 21
Snyder, Y., & Jana, S. (2022) · Biomedical Materials · doi:10.1088/1748-605x/ac94ae · Cited by 19
Kumar, V. R. S., Choudhary, S. K., Radhakrishnan, P. K., Bharath, R. S., Chandrasekaran, N., Sankar, V., Sukumaran, A., & Oommen, C. (2021) · Global Challenges · doi:10.1002/gch2.202000076 · Cited by 18
RetractedIntegration of artificial intelligence into clinical patient management: focus on cardiac imaging
Lončarić, F., Cámara, Ó., Piella, G., & Bijnens, B. H. (2020) · Revista Española de Cardiología (English Edition) · doi:10.1016/j.rec.2020.07.003 · Cited by 18
Fusion of Blockchain, IoT and Artificial Intelligence - A Survey
Koppu, S., Kannan, K., Somayaji, S. R. K., Iyapparaja, M., Wang, W., & Su, C. (2022) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021bcr0001 · Cited by 21
Khatri, S., al-Sulbi, K., Attaallah, A., Ansari, M. T. J., Agrawal, A., & Kumar, R. (2023) · Information · doi:10.3390/info14080425 · Cited by 19
Predictive Modeling for Pandemic Forecasting: A COVID-19 Study in New Zealand and Partner Countries
Baker, O. F., Ziran, Z., Mecella, M., Subaramaniam, K., & Palaniappan, S. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22040562 · Cited by 19
The Possibilities of Blockchain Technology in Medicine (Review)
Litvin, A., Korenev, S. V., Knyazeva, E. G., & Litvin, V. (2019) · Современные технологии в медицине · doi:10.17691/stm2019.11.4.21 · Cited by 15
Blockchain-Enabled Detection of Neurological Disorders Using a Deep Learning Approach
Murthy, K. B. S., & Prasad, S. N. (2024) · International Conference on Recent Advances in Science and Engineering (RAiSE) · doi:10.3390/engproc2023059187 · Cited by 15
Metaverse and Telementoring: From Surgery to Workshop
Ammendola, M., Vescio, F., Ansari, M. A., Hila, J., Rizzo, L. B., Romano, R., Marchegiani, F., de’Angelis, N., Piardi, T., Cavaliere, D., et al. (2024) · Surgical Innovation · doi:10.1177/15533506241233674 · Cited by 13
2024 Chinese guideline on the construction and application of medical blockchain
Chen, X., Cao, F., Wang, Q., Ye, Z., Chen, X., Ye, Z., Cao, F., Chen, W., Cui, W., Feng, D., et al. (2024) · Intelligent Medicine · doi:10.1016/j.imed.2024.09.002 · Cited by 13
Kaur, I., Kumar, Y., & Sandhu, A. K. (2021) · International Conference on Technological Advancements and Innovations (ICTAI) · doi:10.1109/ictai53825.2021.9673285 · Cited by 12
Blokzincir Teknolojisinin Sağlık Bilgi Sistemlerinde Kullanımı
Aydar, M., & Cetin, S. C. (2020) · European Journal of Science and Technology · doi:10.31590/ejosat.735052 · Cited by 12
Self-citationUser acceptability of blockchain technology for enabling electronic health record exchange
Baltruschat, L. M., Jaiman, V., & Urovi, V. (2023) · Journal of Systems and Information Technology · doi:10.1108/jsit-09-2022-0225 · Cited by 14
Emergent Bioanalogous Properties of Blockchain-based Distributed Systems
Abramov, O., Bebell, K. L., & Mojzsis, S. J. (2021) · Origins of Life and Evolution of Biospheres · doi:10.1007/s11084-021-09608-1 · Cited by 12
Future of Electronic Healthcare Management: Blockchain and Artificial Intelligence Integration
Verma, P., Rao, C. M., Chapalamadugu, P. K., Tiwari, R., & Upadhyay, S. (2024) · Blockchain technologies · doi:10.1007/978-981-97-1249-6_9 · Cited by 13
Monlezun, D. J., & MacKay, K. (2024) · Nutrients · doi:10.3390/nu16162601 · Cited by 10
Xie, Y., Wang, X., Yang, H., Zhang, J., Wang, H., Yan, Z., Yang, J., Yan, Z., Hao, Z., Liu, P., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1534117 · Cited by 9
What can Blockchain technology bring to oral and maxillofacial radiology?
Bayrakdar, İ. Ş., Yaşa, Y., Duman, Ş. B., & Orhan, K. (2020) · Oral Surgery Oral Medicine Oral Pathology and Oral Radiology · doi:10.1016/j.oooo.2020.02.012 · Cited by 9
Chin, A. C. (2020) · Frontiers in Blockchain · doi:10.3389/fbloc.2020.606413 · Cited by 9
Integrated approaches to reveal mechanisms by which RNA viruses reprogram the cellular environment
Haddad, C. M., Davila‐Calderon, J., & Tolbert, B. S. (2020) · Methods · doi:10.1016/j.ymeth.2020.06.013 · Cited by 9
Intelligent medicine and beyond
Liu, Z., Shi, Y., Lin, Y., & Yan-xi, Y. (2023) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2022-1240 · Cited by 8
Costa, T., Tiago, M. T. B., Martins, F., & Tiago, F. (2024) · Health Information Management Journal · doi:10.1177/18333583241277952 · Cited by 11
Introduction to artificial intelligence for cardiovascular clinicians
Chang, A. C., & Limon, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00010-x · Cited by 12
Blockchain Empowerment in Healthcare: A Survey
Zhang, J., Yan, M., Tang, Z., He, Q., Bi, Y., Cai, Y., Zhang, Q., Yu, K., Yang, K., Chen, J., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3572962 · Cited by 6
The Banking Industry in the Ecosystem of Digital Currencies and Digital Central Bank Currencies
Jagrič, T., Fister, D., Amon, A., Jagrič, V., & Beloglavec, S. T. (2022) · Contemporary studies in economic and financial analysis · doi:10.1108/s1569-37592022000109a006 · Cited by 6
The scope of metaverse in dermatology
Pathania, Y. S. (2023) · International Journal of Dermatology · doi:10.1111/ijd.16604 · Cited by 6
Safi, M., Kanesvaran, R., Alradhi, M., Al-Danakh, A., Ping, F., Al-Sabai, N., Shan, X., & Liu, J. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.572380 · Cited by 6
Blockchain: chaining digital health to a new era
Yun, D., Chen, W., Wu, X., Ting, D. S. W., & Lin, H. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-2854 · Cited by 6
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
Early outcome detection for COVID-19 patients
Sîrbu, A., Barbieri, G., Faita, F., Ferragina, P., Gargani, L., Ghiadoni, L., & Priami, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-97990-1 · Cited by 5
Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataaS, or Dual?
Jing, Y., Chen, C., Dong, J., & He, Z. (2023) · Systems · doi:10.3390/systems11110550 · Cited by 5
Desroche, L., Trimaille, A., Garban, T., & Bouleti, C. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.07.012 · Cited by 5
Huang, M., Chen, I., & Chung, S. (2022) · BioMed Research International · doi:10.1155/2022/3806431 · Cited by 4
RetractedSpadafora, L., COMANDINI, G. L., Giordano, S. A., Polimeni, A., Perone, F., Sabouret, P. F., Leonetti, M., Cacciatore, S., Cacia, M. A., Betti, M., et al. (2023) · Minerva Cardiology and Angiology · doi:10.23736/s2724-5683.23.06457-8 · Cited by 4
Gomase, V. S., VISOKAR, D. L., & Chopade, V. V. (2025) · Current Drug Discovery Technologies · doi:10.2174/0115701638361312250402125556 · Cited by 3
Mascarenhas, M., Santos, A., & Macedo, G. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99647-1.00011-3 · Cited by 3
Bertoni, A. P. S., Rodrigues, V., Zeiser, F. A., Mello, B., da Costa, C. A., Donida, B., Rigo, S. J., & Righi, R. D. R. (2022) · doi:10.5753/sbc.10508.0.1 · Cited by 3
Why Decentralize Deep Learning?
Wright, S. A. (2023) · International Symposium on Autonomous Decentralized Systems · doi:10.1109/isads56919.2023.10091996 · Cited by 3
Blockchain, Information Security, Control, and Integrity: Who Is in Charge?
Barrera, J. A., Trotsyuk, A. A., Henn, D., Sivaraj, D., Chen, K., Mittal, S., Mermin-Bunnell, A. M., Larson, M. R., Padmanabhan, J., Kinney, B. M., et al. (2023) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000010409 · Cited by 3
Capabilities of Distributed Artificial Intelligence in Medicine
Rumovskaya, S. B., & Litvin, A. (2023) · International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · doi:10.1109/summa60232.2023.10349394 · Cited by 2
A Review on Blockchain Technology with Artificial Intelligence
Bhatia, T. K., Naha, B., Jain, Y., & Gupta, S. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434660 · Cited by 2
AI-powered blockchain technology in healthcare
Taherdoost, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-15728-8.00007-0 · Cited by 2
Blockchain and Clinical Data Economics
Pêgo, A. C., Raposo, I. G., & Loureiro, M. (2021) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-7363-1.ch011 · Cited by 2
Chinyere, O. I., & Oghagbon, E. K. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.626115 · Cited by 2
Alshaibi, A. J., Al-Ani, M. M., & Kadum, J. (2023) · AIP conference proceedings · doi:10.1063/5.0121244 · Cited by 2
Of Machines and Men: Intelligent Diagnosis and the Shape of Things to Come
Cockcroft, J., & Avolio, A. P. (2020) · Current Hypertension Reports · doi:10.1007/s11906-019-1010-3 · Cited by 2
Xiong, K., Li, X., Ding, S., Xie, S., Li, B., Zhang, Y., & Yang, L. (2026) · Collegian Journal of the Royal College of Nursing Australia · doi:10.1016/j.colegn.2026.03.002 · Cited by 1
Blockchain Enable Smart Contract Based Telehealth and Medicines System
Jain, A., & Tripathi, A. K. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-07012-9_60 · Cited by 1
Application in medicine: Has artificial intelligence stood the test of time
Sajid, M. I., Ahmed, S., Waqar, U., Tariq, J., Chundrigarh, M., Balouch, S. S., & Abaidullah, S. (2022) · Chinese Medical Journal · doi:10.1097/cm9.00000000000020s8 · Cited by 5
Emerging trends of blockchain in bioinformatics: A revolution in health care
Balakrishnan, A., Jaglan, P., Selly, S., Kumar, V., & Jabalia, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96146-2.00018-8 · Cited by 3
Was kann die kardiale MR-Bildgebung heute leisten?
Bauer, W. R. (2021) · DMW - Deutsche Medizinische Wochenschrift · doi:10.1055/a-1239-5068 · Cited by 1
Metadata
- Authors
- Krittanawong, C., Rogers, A. J., Aydar, M., Choi, E., Johnson, K. W., Wang, Z., & Narayan, S. M.
- Affiliations
- Baylor College of Medicine (US)
- Stanford Cardiovascular Institute (US)
- Kent State University (US)
- Google (United States) (US)
- Icahn School of Medicine at Mount Sinai (US)
- Mayo Clinic (US)
- Stanford Medicine (US)
- Corresponding author
- Krittanawong, Chayakrit
- Narayan, Sanjiv M.
- ORCID
- Venue
- Nature Reviews Cardiology
- Published
- 2019-10-11
- Volume
- 17
- Issue
- 1
- Pages
- 1-3
- ISSN
- 1759-5002 (ISSN-L)
- 1759-5010
- PMC
- PMC8186804
- OpenAlex
- W2979604013
- Cited by
- 113
- References
- 8
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 14 · 2023: 20 · 2022: 18 · 2021: 24 · 2020: 17 · 2019: 1
- FWCI
- 18.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Cardiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Heart, Lung, and Blood Institute (K24HL103800, F32HL144101, R01HL083359)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- blockchain, artificial intelligence, data interoperability, data silos, data security, information flow, data heterogeneity
- MeSH terms
- Blockchain, Artificial Intelligence, Cardiovascular Diseases, Decision Support Techniques, Diagnosis, Computer-Assisted, Humans, Therapy, Computer-Assisted, Systems Integration, Data Mining, Health Information Management
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