Review Article | | Peer-Reviewed

AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges

Received: 25 May 2025     Accepted: 19 June 2025     Published: 20 August 2025
Views:       Downloads:
Abstract

Background: Cardiovascular diseases (CVD) are the leading cause of mortality in China, accounting for 40% of annual deaths and affecting over 290 million individuals. Rapid urbanization, lifestyle changes, and an aging population have exacerbated CVD risk factors such as hypertension, diabetes, and obesity. Artificial Intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer transformative potential to address these challenges by enhancing diagnostic accuracy, risk stratification, and patient management. Objectives: This systematic review evaluates the effectiveness of AI in improving CVD diagnosis and treatment outcomes within the Chinese healthcare system. Secondary aims include assessing AI’s role in risk prediction, identifying implementation barriers, and exploring future directions. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search (2021-2025) across PubMed, CNKI, IEEE Xplore, Scopus, and Web of Science. Inclusion criteria focused on peer-reviewed studies involving AI applications (ML/DL) in adult CVD care, while excluding non-empirical research or studies outside China. Results: Preliminary findings demonstrate that AI significantly enhances diagnostic precision (e.g., CNNs for ECG interpretation, DL for imaging analysis) and enables personalized treatment plans. Challenges include infrastructural limitations, data privacy concerns, and clinician resistance due to inadequate training. AI-driven predictive analytics show promise in early intervention but require robust validation and ethical oversight. Conclusion: AI holds immense potential to revolutionize CVD care in China, though its integration demands addressing technological, educational, and ethical barriers. Future research should prioritize longitudinal studies and standardized frameworks to ensure equitable, transparent AI deployment in cardiology.

Published in Clinical Medicine Research (Volume 14, Issue 4)
DOI 10.11648/j.cmr.20251404.15
Page(s) 136-144
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence, Cardiovascular Diseases, Machine Learning, Deep Learning, China, Risk Stratification, Predictive Analytics

References
[1] Cardiovascular risk factors in China: a nationwide population-based cohort study Li, Xi et al. The Lancet Public Health, Volume 5, Issue 12, e672 - e681.
[2] Chan F, Adamo S, Coxson P, Goldman L, Gu D, Zhao D, Chen CS, He J, Mara V, Moran A. Projected impact of urbanization on cardiovascular disease in China. Int J Public Health. 2012 Oct; 57(5): 849-54.
[3] Zhao D, Liu J, Wang M, Zhang X, Zhou M. Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol. 2019 Apr; 16(4): 203-212.
[4] Johnson, K, Torres Soto, J, Glicksberg, B. et al. Artificial Intelligence in Cardiology. JACC. 2018 Jun, 71(23) 2668-2679.
[5] Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel). 2024 Feb 16; 12(4): 481.
[6] Cicek V, Cikirikci EHK, Babaoğlu M, Erdem A, Tur Y, Mohamed MI, Cinar T, Savas H, Bagci U. Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis. EJNMMI Res. 2024 Nov 26; 14(1): 117.
[7] Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med. 2024 Feb 5; 22(1): 56.
[8] Vandenberk B, Chew DS, Prasana D, Gupta S, Exner DV. Successes and challenges of artificial intelligence in cardiology. Front Digit Health. 2023 Jun 28; 5: 1201392.
[9] Lewin, S., Chetty, R., Ihdayhid, A. R., & Dwivedi, G. (2024). Ethical challenges and opportunities in applying artificial intelligence to cardiovascular medicine. Canadian Journal of Cardiology, 40(10), 1897-1906.
[10] Itchhaporia D. Artificial intelligence in cardiology. Trends Cardiovasc Med. 2022 Jan; 32(1): 34-41.
[11] Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, Deswal A, Drazner MH, Dunlay SM, Evers LR, Fang JC, Fedson SE, Fonarow GC, Hayek SS, Hernandez AF, Khazanie P, Kittleson MM, Lee CS, Link MS, Milano CA, Nnacheta LC, Sandhu AT, Stevenson LW, Vardeny O, Vest AR, Yancy CW; ACC/AHA Joint Committee Members. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022 May 3; 145(18): e895-e1032.
[12] Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, Kapa S, Lerman A, Luong C, Medina-Inojosa JR, Noseworthy PA, Pellikka PA, Redfield MM, Roger VL, Sandhu GS, Senecal C, Friedman PA. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020 May; 95(5): 1015-1039.
[13] Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017 May 30; 69(21): 2657-2664.
[14] Youn, J.-C., Kim, D., Cho, J. Y., Cho, D.-H., Park, S. M., Jung, M.-H., Hyun, J., Cho, H.-J., Park, S.-M., Choi, J.-O., Chung, W.-J., Yoo, B.-S., & Kang, S.-M. (2023). Korean Society of Heart Failure Guidelines for the Management of Heart Failure: Treatment. Korean Circulation Journal, 53(4), 217-238.
[15] Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, Marwick TH. Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review. J Am Coll Cardiol. 2019 Mar 26; 73(11): 1317-1335.
[16] El Sherbini A, Rosenson RS, Al Rifai M, Virk HUH, Wang Z, Virani S, Glicksberg BS, Lavie CJ, Krittanawong C. Artificial intelligence in preventive cardiology. Prog Cardiovasc Dis. 2024 May-Jun; 84: 76-89.
[17] Bart NK, Pepe S, Gregory AT, Denniss AR. Emerging Roles of Artificial Intelligence (AI) in Cardiology: Benefits and Barriers in a 'Brave New World'. Heart Lung Circ. 2023 Aug; 32(8): 883-888.
[18] Itelman E, Witberg G, Kornowski R. AI-Assisted Clinical Decision Making in Interventional Cardiology: The Potential of Commercially Available Large Language Models. JACC Cardiovasc Interv. 2024 Aug 12; 17(15): 1858-1860.
[19] Skalidis I, Kachrimanidis I, Koliastasis L, Arangalage D, Antiochos P, Maurizi N, Muller O, Fournier S, Hamilos M, Skalidis E. Cardiology in the digital era: from artificial intelligence to Metaverse, paving the way for future advancements. Future Cardiol. 2023 Dec; 19(16): 755-758.
[20] Danilov A, Aronow WS. Artificial Intelligence in Cardiology: Applications and Obstacles. Curr Probl Cardiol. 2023 Sep; 48(9): 101750.
[21] Salihu A, Gadiri MA, Skalidis I, Meier D, Auberson D, Fournier A, Fournier R, Thanou D, Abbé E, Muller O, Fournier S. Towards AI-assisted cardiology: a reflection on the performance and limitations of using large language models in clinical decision-making. EuroIntervention. 2023 Dec 4; 19(10): e798-e801.
[22] Watson X, D'Souza J, Cooper D, Markham R. Artificial intelligence in cardiology: fundamentals and applications. Intern Med J. 2022 Jun; 52(6): 912-920.
[23] Chaudhary R, Harinstein ME. Leveraging Artificial Intelligence in Cardiology: Interaction Between Atrial Fibrillation and Cardiopulmonary Dynamics. Am J Cardiol. 2023 Oct 15; 205: 497-498.
[24] Savarese G, Lindberg F, Christodorescu RM, Ferrini M, Kumler T, Toutoutzas K, Dattilo G, Bayes-Genis A, Moura B, Amir O, Petrie MC, Seferovic P, Chioncel O, Metra M, Coats AJS, Rosano GMC. Physician perceptions, attitudes, and strategies towards implementing guideline-directed medical therapy in heart failure with reduced ejection fraction. A survey of the Heart Failure Association of the ESC and the ESC Council for Cardiology Practice. Eur J Heart Fail. 2024 Jun; 26(6): 1408-1418.
[25] Miller RJH, Slomka PJ. Artificial Intelligence in Nuclear Cardiology: An Update and Future Trends. Semin Nucl Med. 2024 Sep; 54(5): 648-657.
[26] Rudnicka Z, Pręgowska A, Glądys K, Perkins M, Proniewska K. Advancements in artificial intelligence-driven techniques for interventional cardiology. Cardiol J. 2024; 31(2): 321-341.
[27] Hamayun S, Hameed H, Rehman AU, Amin Z, Malik MN. Innovations in interventional cardiology: Pioneering techniques for a new era. Curr Probl Cardiol. 2024 Dec; 49(12): 102836.
[28] Chinese Society of Cardiology. (2025). Chinese guidelines for the diagnosis and treatment of heart failure 2024. Cardiology Discovery, 5(1), 1-38.
[29] Dorado-Díaz PI, Sampedro-Gómez J, Vicente-Palacios V, Sánchez PL. Applications of Artificial Intelligence in Cardiology. The Future is Already Here. Rev Esp Cardiol (Engl Ed). 2019 Dec; 72(12): 1065-1075. English, Spanish.
[30] Menown IA, Shand JA. Recent advances in cardiology. Future Cardiol. 2010 Jan; 6(1): 11-7. Erratum in: Future Cardiol. 2012 Jan; 8(1): 141-2.
[31] Cappato R, Marchlinski FE, Hohnloser SH, Naccarelli GV, Xiang J, Wilber DJ, Ma CS, Hess S, Wells DS, Juang G, Vijgen J, Hügl BJ, Balasubramaniam R, De Chillou C, Davies DW, Fields LE, Natale A; VENTURE-AF Investigators. Uninterrupted rivaroxaban vs. uninterrupted vitamin K antagonists for catheter ablation in non-valvular atrial fibrillation. Eur Heart J. 2015 Jul 21; 36(28): 1805-11.
[32] You SC, Yao X, Bikdeli B, Spatz ES. Embracing Change: Human-Centered Cardiovascular Medicine in the Era of AI. J Am Coll Cardiol. 2024 Oct 8; 84(15): 1495-1497.
[33] Langlais ÉL, Thériault-Lauzier P, Marquis-Gravel G, Kulbay M, So DY, Tanguay JF, Ly HQ, Gallo R, Lesage F, Avram R. Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications. J Cardiovasc Transl Res. 2023 Jun; 16(3): 513-525.
[34] Parsa, S., Shah, P., Doijad, R. et al. Artificial Intelligence in Ischemic Heart Disease Prevention. Curr Cardiol Rep 27, 44(2025).
[35] Otaki Y, Miller RJH, Slomka PJ. The application of artificial intelligence in nuclear cardiology. Ann Nucl Med. 2022 Feb; 36(2): 111-122.
[36] Inam M, Sheikh S, Minhas AMK, Vaughan EM, Krittanawong C, Samad Z, Lavie CJ, Khoja A, D'Cruze M, Slipczuk L, Alarakhiya F, Naseem A, Haider AH, Virani SS. A review of top cardiology and cardiovascular medicine journal guidelines regarding the use of generative artificial intelligence tools in scientific writing. Curr Probl Cardiol. 2024 Mar; 49(3): 102387.
[37] Pieszko K, Hiczkiewicz J, Budzianowski J, Musielak B, Hiczkiewicz D, Faron W, Rzeźniczak J, Burchardt P. Clinical applications of artificial intelligence in cardiology on the verge of the decade. Cardiol J. 2021; 28(3): 460-472.
[38] Jain SS, Elias P, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein AJ, Avram R, Tison GH, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week. J Am Coll Cardiol. 2024 Jun 18; 83(24): 2487-2496.
[39] Wang H, Zu Q, Lu M, Chen R, Yang Z, Gao Y, Ding J. Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review. Adv Ther. 2022 Sep; 39(9): 4052-4060.
[40] Chowdhury D, Hope KD, Arthur LC, Weinberger SM, Ronai C, Johnson JN, Snyder CS. Telehealth for Pediatric Cardiology Practitioners in the Time of COVID-19. Pediatr Cardiol. 2020 Aug; 41(6): 1081-1091.
[41] Sliwa K, Hilfiker-Kleiner D, Petrie MC, Mebazaa A, Pieske B, Buchmann E, Regitz-Zagrosek V, Schaufelberger M, Tavazzi L, van Veldhuisen DJ, Watkins H, Shah AJ, Seferovic PM, Elkayam U, Pankuweit S, Papp Z, Mouquet F, McMurray JJ; Heart Failure Association of the European Society of Cardiology Working Group on Peripartum Cardiomyopathy. Current state of knowledge on aetiology, diagnosis, management, and therapy of peripartum cardiomyopathy: a position statement from the Heart Failure Association of the European Society of Cardiology Working Group on peripartum cardiomyopathy. Eur J Heart Fail. 2010 Aug; 12(8): 767-78.
[42] Gala D, Makaryus AN. The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4. Int J Environ Res Public Health. 2023 Jul 25; 20(15): 6438.
[43] Kulkarni P, Mahadevappa M, Chilakamarri S. The Emergence of Artificial Intelligence in Cardiology: Current and Future Applications. Curr Cardiol Rev. 2022; 18(3): e191121198124.
[44] Van den Eynde J, Lachmann M, Laugwitz KL, Manlhiot C, Kutty S. Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends Cardiovasc Med. 2023 Jul; 33(5): 265-271.
[45] Howe AJ, Shand JA, Menown IB. Advances in cardiology: clinical trial update. Future Cardiol. 2011 May; 7(3): 299-310.
[46] Miller RJH, Slomka PJ. Current status and future directions in artificial intelligence for nuclear cardiology. Expert Rev Cardiovasc Ther. 2024 Aug; 22(8): 367-378.
[47] Miller RJH, Huang C, Liang JX, Slomka PJ. Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. J Nucl Cardiol. 2022 Aug; 29(4): 1754-1762.
[48] Miller DD. Machine Intelligence in Cardiovascular Medicine. Cardiol Rev. 2020 Mar/Apr; 28(2): 53-64.
[49] Huwiler J, Oechslin L, Biaggi P, Tanner FC, Wyss CA. Experimental assessment of the performance of artificial intelligence in solving multiple-choice board exams in cardiology. Swiss Med Wkly. 2024 Oct 2; 154: 3547.
[50] Arrigo M, Price S, Harjola VP, Huber LC, Schaubroeck HAI, Vieillard-Baron A, Mebazaa A, Masip J. Diagnosis and treatment of right ventricular failure secondary to acutely increased right ventricular afterload (acute cor pulmonale): a clinical consensus statement of the Association for Acute CardioVascular Care of the European Society of Cardiology. Eur Heart J Acute Cardiovasc Care. 2024 Mar 11; 13(3): 304-312.
Cite This Article
  • APA Style

    Ismael, A. M. A., Azzawi, M. A. K. H., Abdulaaima, A. M. A. J., Mohammed, M. I. M., Ullah, M., et al. (2025). AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges. Clinical Medicine Research, 14(4), 136-144. https://doi.org/10.11648/j.cmr.20251404.15

    Copy | Download

    ACS Style

    Ismael, A. M. A.; Azzawi, M. A. K. H.; Abdulaaima, A. M. A. J.; Mohammed, M. I. M.; Ullah, M., et al. AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges. Clin. Med. Res. 2025, 14(4), 136-144. doi: 10.11648/j.cmr.20251404.15

    Copy | Download

    AMA Style

    Ismael AMA, Azzawi MAKH, Abdulaaima AMAJ, Mohammed MIM, Ullah M, et al. AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges. Clin Med Res. 2025;14(4):136-144. doi: 10.11648/j.cmr.20251404.15

    Copy | Download

  • @article{10.11648/j.cmr.20251404.15,
      author = {Alattabi Mustafa Allawi Ismael and Mohammed Ali Khudhair Hashim Azzawi and Al Mohsen Abbas Jawad Abdulaaima and Mohammed Imad Mohammed Mohammed and Muneeb Ullah and Haq Ihtishamul},
      title = {AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges
    },
      journal = {Clinical Medicine Research},
      volume = {14},
      number = {4},
      pages = {136-144},
      doi = {10.11648/j.cmr.20251404.15},
      url = {https://doi.org/10.11648/j.cmr.20251404.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20251404.15},
      abstract = {Background: Cardiovascular diseases (CVD) are the leading cause of mortality in China, accounting for 40% of annual deaths and affecting over 290 million individuals. Rapid urbanization, lifestyle changes, and an aging population have exacerbated CVD risk factors such as hypertension, diabetes, and obesity. Artificial Intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer transformative potential to address these challenges by enhancing diagnostic accuracy, risk stratification, and patient management. Objectives: This systematic review evaluates the effectiveness of AI in improving CVD diagnosis and treatment outcomes within the Chinese healthcare system. Secondary aims include assessing AI’s role in risk prediction, identifying implementation barriers, and exploring future directions. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search (2021-2025) across PubMed, CNKI, IEEE Xplore, Scopus, and Web of Science. Inclusion criteria focused on peer-reviewed studies involving AI applications (ML/DL) in adult CVD care, while excluding non-empirical research or studies outside China. Results: Preliminary findings demonstrate that AI significantly enhances diagnostic precision (e.g., CNNs for ECG interpretation, DL for imaging analysis) and enables personalized treatment plans. Challenges include infrastructural limitations, data privacy concerns, and clinician resistance due to inadequate training. AI-driven predictive analytics show promise in early intervention but require robust validation and ethical oversight. Conclusion: AI holds immense potential to revolutionize CVD care in China, though its integration demands addressing technological, educational, and ethical barriers. Future research should prioritize longitudinal studies and standardized frameworks to ensure equitable, transparent AI deployment in cardiology.},
     year = {2025}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges
    
    AU  - Alattabi Mustafa Allawi Ismael
    AU  - Mohammed Ali Khudhair Hashim Azzawi
    AU  - Al Mohsen Abbas Jawad Abdulaaima
    AU  - Mohammed Imad Mohammed Mohammed
    AU  - Muneeb Ullah
    AU  - Haq Ihtishamul
    Y1  - 2025/08/20
    PY  - 2025
    N1  - https://doi.org/10.11648/j.cmr.20251404.15
    DO  - 10.11648/j.cmr.20251404.15
    T2  - Clinical Medicine Research
    JF  - Clinical Medicine Research
    JO  - Clinical Medicine Research
    SP  - 136
    EP  - 144
    PB  - Science Publishing Group
    SN  - 2326-9057
    UR  - https://doi.org/10.11648/j.cmr.20251404.15
    AB  - Background: Cardiovascular diseases (CVD) are the leading cause of mortality in China, accounting for 40% of annual deaths and affecting over 290 million individuals. Rapid urbanization, lifestyle changes, and an aging population have exacerbated CVD risk factors such as hypertension, diabetes, and obesity. Artificial Intelligence (AI) technologies, including machine learning (ML) and deep learning (DL), offer transformative potential to address these challenges by enhancing diagnostic accuracy, risk stratification, and patient management. Objectives: This systematic review evaluates the effectiveness of AI in improving CVD diagnosis and treatment outcomes within the Chinese healthcare system. Secondary aims include assessing AI’s role in risk prediction, identifying implementation barriers, and exploring future directions. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search (2021-2025) across PubMed, CNKI, IEEE Xplore, Scopus, and Web of Science. Inclusion criteria focused on peer-reviewed studies involving AI applications (ML/DL) in adult CVD care, while excluding non-empirical research or studies outside China. Results: Preliminary findings demonstrate that AI significantly enhances diagnostic precision (e.g., CNNs for ECG interpretation, DL for imaging analysis) and enables personalized treatment plans. Challenges include infrastructural limitations, data privacy concerns, and clinician resistance due to inadequate training. AI-driven predictive analytics show promise in early intervention but require robust validation and ethical oversight. Conclusion: AI holds immense potential to revolutionize CVD care in China, though its integration demands addressing technological, educational, and ethical barriers. Future research should prioritize longitudinal studies and standardized frameworks to ensure equitable, transparent AI deployment in cardiology.
    VL  - 14
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Sections