Early Detection of Anthracycline-Induced Cardiomyopathy: A Narrative Review of the Role of Artificial Intelligence and Remote Monitoring.

Anthracyclines remain central to the treatment of many malignancies, yet their use is limited by the risk of anthracycline-induced cardiomyopathy (AIC), a complication associated with substantial long-term cardiovascular morbidity and mortality. The occurrence of cardiotoxicity during cancer treatment may adversely affect both treatment success rates and patient survival, especially when dose reduction or premature discontinuation of anthracycline therapy is necessitated, leading to treatment discontinuity, reduced therapeutic efficacy, and overall reduced quality of life. While anthracyclines improve cancer outcomes, the resulting cardiac damage may lead to heart failure, disability, repeated hospitalizations, and premature death, with the long-term cardiovascular complications often becoming a greater determinant of survival than the original cancer itself. Growing understanding of the biological mechanisms underlying anthracycline cardiotoxicity has improved the recognition of patients at increased risk and clarified the progressive nature of this condition, which typically evolves from subclinical myocardial injury to left ventricular dysfunction and, if unrecognized, overt heart failure. Risk stratification has traditionally relied on cardiac imaging and circulating biomarkers. Although some current surveillance methods for cardiotoxicity monitoring detect myocardial damage at subclinical and partially or fully reversible stages, many traditional methods are relatively late markers and detect myocardial damage usually after it reaches a stage where treatment becomes unable to reverse the damage. The limitations of left ventricular ejection fraction as a sole monitoring parameter have prompted increasing use of more sensitive imaging markers and biochemical indicators of early myocardial injury. This review conducts a medical evaluation of AIC to study triggers, patient manifestations, and risk factors that develop during treatment. It evaluates present diagnostic methods, including echocardiography, cardiac magnetic resonance imaging, and cardiac biomarkers, and investigates new methods that enable healthcare practitioners to detect diseases at their initial stages through artificial intelligence (AI)-based analytical tools, wearable technologies, and remote patient monitoring systems. Finally, we consider future directions aimed at earlier identification and personalized prevention of anthracycline-related cardiac dysfunction.