THE ROLE OF ARTIFICIAL INTELLIGENCE IN EARLY DETECTION OF HEART DISEASES: SOCIAL IMPLICATIONS AND ETHICAL CHALLENGES
Abstract
The dynamic development of modern technologies, particularly artificial intelligence (AI), is significantly impacting the functioning of modern healthcare systems, opening up new opportunities for the early detection of cardiovascular diseases, which remain one of the leading causes of death worldwide. Lifestyle changes associated with technological advancements are contributing to the rise in the incidence of lifestyle diseases, significantly increasing the risk of developing cardiovascular disease. In this context, early diagnosis and effective prevention are becoming key public health priorities.
The aim of this paper is to analyze the role of AI in the early detection of heart disease, taking into account its medical, social, and ethical aspects. Methods based on machine and deep learning enable the analysis of large and complex medical datasets, such as electrocardiograms, imaging studies, electronic medical records, and data from wearable devices. The use of AI allows for the identification of subtle pathological changes that often appear before the onset of clinical symptoms, supporting the transition from reactive to preventive and personalized medicine.
At the same time, the implementation of AI systems for cardiology screening has significant social implications. While these technologies can increase the availability of early diagnosis and improve the utilization of healthcare resources, there is a risk of exacerbating health inequalities resulting from algorithmic biases, limited data representativeness, and the digital divide. Furthermore, ethical challenges arise related to the protection of sensitive data, the transparency and explainability of algorithms, and accountability for clinical decisions.
The paper emphasizes that artificial intelligence should serve as a tool supporting clinical decisions, while maintaining the physician's central role in the diagnostic and therapeutic process. Responsible integration of AI requires building trust, transparent communication with patients, and the creation of a coherent regulatory framework. The societal value of artificial intelligence in cardiology depends on its ethical, equitable, and human-centered implementation.
References
Wysocka, A., Potyra, M., Góral-Radziszewska, K., Waśkiewicz, K., & Gawińska-Drużba, E. (2025). Life expectancy in Poland 2024 (Report). Główny Urząd Statystyczny.
Khan, M. S., Sreenivasan, J., Lateef, N., et al. (2024). The role of artificial intelligence in heart failure diagnostics, risk prediction, and therapeutic strategies: A comprehensive review. Cureus, 16(7), e87130. https://doi.org/10.7759/cureus.87130
Attia, Z. I., Friedman, P. A., Noseworthy, P. A., et al. (2017). Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Journal of the American College of Cardiology, 69(21), 2657–2668. https://doi.org/10.1016/j.jacc.2017.03.571
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., et al. (2018). An artificial intelligence–enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm. Journal of the American College of Cardiology, 71(19), 2112–2120. https://doi.org/10.1016/j.jacc.2018.03.521
Zhang, Y., Zhang, X., Liu, C., et al. (2022). Artificial intelligence in cardiovascular medicine: Current status and future perspectives. Reviews in Cardiovascular Medicine, 22(4), 121. https://doi.org/10.31083/j.rcm2204121
Patel, N., Shah, M., Desai, R., et al. (2024). Ethical and social challenges of artificial intelligence in cardiovascular care. Cureus, 16(9), e98378. https://doi.org/10.7759/cureus.98378
European Society of Cardiology. (2025). Artificial intelligence in cardiovascular medicine: Policy and clinical implications. European Heart Journal. Advance online publication. https://doi.org/10.1093/eurheartj/ehaf125
Johnson, K. W., Soto, J. T., Glicksberg, B. S., et al. (2025). Artificial intelligence in preventive cardiology. Heart Failure Clinics, 21(2), 115–128. https://doi.org/10.1016/j.hfc.2025.01.015
Topol, E. J., Rajpurkar, P., Lee, C. S., et al. (2025). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. Advance online publication. https://doi.org/10.1038/s41591-025-03517-w
Demir, M., Aydın, S., & Yılmaz, A. (2024). Artificial intelligence applications in cardiovascular imaging. Diagnostic and Interventional Radiology. Advance online publication. https://doi.org/10.4274/dir.2024.242854
Jones, D. S., Greene, J. A., & Kesselheim, A. S. (2024). Ethical challenges of AI implementation in healthcare systems. BMJ Open, 14(8), e089090. https://doi.org/10.1136/bmjopen-2024-089090
McDougall, R. J., Fogarty, J., & Miller, T. (2024). Patient trust and artificial intelligence in healthcare decision-making. Patient Education and Counseling, 120, 108619. https://doi.org/10.1016/j.pec.2024.108619
Copyright (c) 2026 Paweł Słoma, Paulina Dybiak, Adrian Morawiec, Maciej Zachara, Mateusz Bartoszek, Patryk Harnicki, Mikołaj Grodzki, Jakub Minas, Erwin Grzegorzak, Rafał Pelczar, Oliwia Krawczyk
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