The Role of Artificial Intelligence in Early Diagnosis and Management of Cardiovascular Diseases
DOI:
https://doi.org/10.70749/ijbr.v3i2.667Keywords:
Artificial Intelligence, Early Diagnosis, Cardiovascular Diseases, Personalized Treatment, Healthcare CostsAbstract
The increasing rate of cardiovascular diseases (CVDs) has posed a tremendous challenge to their early detection and personalized treatment. This research examines the potential of Artificial Intelligence (AI) for early detection and management of CVDs, in particular whether it can enhance diagnostic accuracy, personalize treatment guidelines, and reduce healthcare costs. A quantitative methodology was adopted and a survey strategy was employed for collecting primary data from 300 healthcare professionals consisting of cardiologists, general physicians, and professionals in AI fields from Punjab hospitals in Pakistan. The questionnaire was constructed to determine their knowledge, experiences, and perceptions regarding the use of AI in cardiovascular services. Data analysis revealed that application of AI had a strong correlation with increased diagnostic success, evident in a statistically significant chi-square test (p < 0.001). Furthermore, multiple regression analysis revealed that AI, together with years of experience and educational history, is an important contributor to personalizing cardiovascular treatment plans. The results indicate that AI has a key role in making more precise diagnoses and improving treatment methods, which can ultimately decrease the cost of healthcare and enhance patient outcomes. Yet, issues around data privacy, transparency, and clinician confidence in AI systems must be resolved in order for AI to be adopted more widely. Future research is suggested by the study into the integration of AI with other health technologies and the ethics of using AI in clinical practice.
Downloads
References
Addissouky, T. A., El Sayed, I. E., Ali, M. M., Alubiady, M. H., & Wang, Y. (2024). Recent developments in the diagnosis, treatment, and management of cardiovascular diseases through artificial intelligence and other innovative approaches. Journal of Biomed Research, 5(1), 29-40. https://doi.org/10.46439/biomedres.5.041
Almansouri, N. E., Awe, M., Rajavelu, S., Jahnavi, K., Shastry, R., Hasan, A., Hasan, H., Lakkimsetti, M., AlAbbasi, R. K., Gutiérrez, B. C., & Haider, A. (2024). Early diagnosis of cardiovascular diseases in the era of artificial intelligence: An in-depth review. Cureus. https://doi.org/10.7759/cureus.55869
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology, 18(7), 465-478. https://doi.org/10.1038/s41569-020-00503-2
Romiti, S., Vinciguerra, M., Saade, W., Anso Cortajarena, I., & Greco, E. (2020). Artificial intelligence (AI) and cardiovascular diseases: An unexpected alliance. Cardiology Research and Practice, 2020, 1-8. https://doi.org/10.1155/2020/4972346
Yasmin, F., Shah, S. M., Naeem, A., Shujauddin, S. M., Jabeen, A., Kazmi, S., Siddiqui, S. A., Kumar, P., Salman, S., Hassan, S. A., Dasari, C., Choudhry, A. S., Mustafa, A., Chawla, S., & Lak, H. M. (2021). Artificial intelligence in the diagnosis and detection of heart failure: The past, present, and future. Reviews in Cardiovascular Medicine, 22(4). https://doi.org/10.31083/j.rcm2204121
Muzammil, M. A., Javid, S., Afridi, A. K., Siddineni, R., Shahabi, M., Haseeb, M., Fariha, F., Kumar, S., Zaveri, S., & Nashwan, A. J. (2024). Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. Journal of Electrocardiology, 83, 30-40. https://doi.org/10.1016/j.jelectrocard.2024.01.006
Srinivasan, S. M., & Sharma, V. (2025). Applications of AI in cardiovascular disease detection — A review of the specific ways in which AI is being used to detect and diagnose cardiovascular diseases. AI in Disease Detection, 123-146. https://doi.org/10.1002/9781394278695.ch6
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020). Artificial intelligence, machine learning, and cardiovascular disease. Clinical Medicine Insights: Cardiology, 14, 117954682092740. https://doi.org/10.1177/1179546820927404
Kagiyama, N., Shrestha, S., Farjo, P. D., & Sengupta, P. P. (2019). Artificial intelligence: Practical primer for clinical research in cardiovascular disease. Journal of the American Heart Association, 8(17). https://doi.org/10.1161/jaha.119.012788
Baghdadi, N. A., Farghaly Abdelaliem, S. M., Malki, A., Gad, I., Ewis, A., & Atlam, E. (2023). Advanced machine learning techniques for cardiovascular disease early detection and diagnosis. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00817-1
Samanidis, G. (2024). Current challenges in diagnosis and treatment of cardiovascular disease. Journal of Personalized Medicine, 14(8), 786. https://doi.org/10.3390/jpm14080786
Visco, V. (2021). Artificial intelligence as a business partner in cardiovascular precision medicine: An emerging approach for disease detection and treatment optimization. Current Medicinal Chemistry, 28(32), 6569-6590. https://doi.org/10.2174/1875533xmtey3ntmax
Ekundayo, F., & Nyavor, H. (2024). AI-driven predictive analytics in cardiovascular diseases: Integrating big data and machine learning for early diagnosis and risk prediction. International Journal of Research Publication and Reviews, 5(12), 1240-1256. https://doi.org/10.55248/gengpi.5.1224.3437
Zaman, Q., The Role of Artificial Intelligence in Early Disease Detection: Transforming Diagnostics and Treatment. Multidisciplinary Journal of Healthcare (MJH), 2024. 1(2): p. 43-54.
Harjai, S., & Khatri, S. K. (2019). An intelligent clinical decision support system based on artificial neural network for early diagnosis of cardiovascular diseases in rural areas. 2019 Amity International Conference on Artificial Intelligence (AICAI), 729-736. https://doi.org/10.1109/aicai.2019.8701237
Huang, J., Wang, J., Ramsey, E., Leavey, G., Chico, T. J., & Condell, J. (2022). Applying artificial intelligence to wearable sensor data to diagnose and predict cardiovascular disease: A review. Sensors, 22(20), 8002. https://doi.org/10.3390/s22208002
Adedinsewo, D. A., Pollak, A. W., Phillips, S. D., Smith, T. L., Svatikova, A., Hayes, S. N., Mulvagh, S. L., Norris, C., Roger, V. L., Noseworthy, P. A., Yao, X., & Carter, R. E. (2022). Cardiovascular disease screening in women: Leveraging artificial intelligence and digital tools. Circulation Research, 130(4), 673-690. https://doi.org/10.1161/circresaha.121.319876
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. https://doi.org/10.1016/j.jacc.2017.03.571
Mhamdi, L., Dammak, O., Cottin, F., & Dhaou, I. B. (2022). Artificial intelligence for cardiac diseases diagnosis and prediction using ECG images on embedded systems. Biomedicines, 10(8), 2013. https://doi.org/10.3390/biomedicines10082013
Miller, R. J., Huang, C., Liang, J. X., & Slomka, P. J. (2022). Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology. Journal of Nuclear Cardiology, 29(4), 1754-1762. https://doi.org/10.1007/s12350-022-02977-8
Athar, M. (2024). Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment. International Journal of Cardiology, 412, 132315. https://doi.org/10.1016/j.ijcard.2024.132315
Tique, M. R., Araque, O., & Sanchez-Echeverri, L. A. (2024). Technological advances in the diagnosis of cardiovascular disease: A public health strategy. International Journal of Environmental Research and Public Health, 21(8), 1083. https://doi.org/10.3390/ijerph21081083
Reddy, C. D., Van den Eynde, J., & Kutty, S. (2022). Artificial intelligence in perinatal diagnosis and management of congenital heart disease. Seminars in Perinatology, 46(4), 151588. https://doi.org/10.1016/j.semperi.2022.151588
Singhal, S., & Kumar, M. (2022). A systematic review on artificial intelligence-based techniques for diagnosis of cardiovascular arrhythmia diseases: Challenges and opportunities. Archives of Computational Methods in Engineering, 30(2), 865-888. https://doi.org/10.1007/s11831-022-09823-7
Al-Maini, M., Maindarkar, M., Kitas, G. D., Khanna, N. N., Misra, D. P., Johri, A. M., Mantella, L., Agarwal, V., Sharma, A., Singh, I. M., Tsoulfas, G., Laird, J. R., Faa, G., Teji, J., Turk, M., Viskovic, K., Ruzsa, Z., Mavrogeni, S., Rathore, V., … Suri, J. S. (2023). Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: A narrative review. Rheumatology International, 43(11), 1965-1982. https://doi.org/10.1007/s00296-023-05415-1
Ghaffar Nia, N., Kaplanoglu, E., & Nasab, A. (2023). Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence, 3(1). https://doi.org/10.1007/s44163-023-00049-5
Alizadehsani, R., Khosravi, A., Roshanzamir, M., Abdar, M., Sarrafzadegan, N., Shafie, D., Khozeimeh, F., Shoeibi, A., Nahavandi, S., Panahiazar, M., Bishara, A., Beygui, R. E., Puri, R., Kapadia, S., Tan, R., & Acharya, U. R. (2021). Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991–2020. Computers in Biology and Medicine, 128, 104095. https://doi.org/10.1016/j.compbiomed.2020.104095
Seetharam, K., Brito, D., Farjo, P. D., & Sengupta, P. P. (2020). The role of artificial intelligence in cardiovascular imaging: State of the art review. Frontiers in Cardiovascular Medicine, 7. https://doi.org/10.3389/fcvm.2020.618849
Elvas, L. B., Nunes, M., Ferreira, J. C., Dias, M. S., & Rosário, L. B. (2023). AI-driven decision support for early detection of cardiac events: Unveiling patterns and predicting myocardial ischemia. Journal of Personalized Medicine, 13(9), 1421. https://doi.org/10.3390/jpm13091421
Laudicella, R. (2021). Artificial neural networks in cardiovascular diseases and its potential for clinical application in molecular imaging. Current Radiopharmaceuticals, 14(3), 209-219. https://doi.org/10.2174/18744729mta3dntiz4
Bouris, V., & Avgerinos, E. (2024). AI applications in cardiovascular disease diagnosis and management. Advances in Medical Technologies and Clinical Practice, 169-184. https://doi.org/10.4018/979-8-3693-4422-4.ch009
Chlorogiannis, D., Apostolos, A., Chlorogiannis, A., Palaiodimos, L., Giannakoulas, G., Pargaonkar, S., Xesfingi, S., & Kokkinidis, D. G. (2023). The role of ChatGPT in the advancement of diagnosis, management, and prognosis of cardiovascular and cerebrovascular disease. Healthcare, 11(21), 2906. https://doi.org/10.3390/healthcare11212906
Nedadur, R., Wang, B., & Tsang, W. (2022). Artificial intelligence for the echocardiographic assessment of valvular heart disease. Heart, 108(20), 1592-1599. https://doi.org/10.1136/heartjnl-2021-319725
Kwon, J., Kim, K., Eisen, H. J., Cho, Y., Jeon, K., Lee, S. Y., Park, J., & Oh, B. (2020). Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features. European Heart Journal - Digital Health, 2(1), 106-116. https://doi.org/10.1093/ehjdh/ztaa015
Batta, I., Patial, R., C Sobti, R., & K Agrawal, D. (2024). Computational biology in the discovery of biomarkers in the diagnosis, treatment and management of cardiovascular diseases. Cardiology and Cardiovascular Medicine, 8(5). https://doi.org/10.26502/fccm.92920400
Upton, R., Mumith, A., Beqiri, A., Parker, A., Hawkes, W., Gao, S., Porumb, M., Sarwar, R., Marques, P., Markham, D., Kenworthy, J., O’Driscoll, J. M., Hassanali, N., Groves, K., Dockerill, C., Woodward, W., Alsharqi, M., McCourt, A., Wilkes, E. H., … Leeson, P. (2022). Automated Echocardiographic detection of severe coronary artery disease using artificial intelligence. JACC: Cardiovascular Imaging, 15(5), 715-727. https://doi.org/10.1016/j.jcmg.2021.10.013
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Indus Journal of Bioscience Research

This work is licensed under a Creative Commons Attribution 4.0 International License.