Libro

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, accounting for approximately 17.9 million deaths annually. Traditional healthcare systems face significant challenges including delayed diagnoses, inadequate care accessibility, and rising medical costs. The emergence of machine learning-enhanced point-of-care (POC) devices represents a transformative approach to cardiovascular healthcare, offering rapid diagnostic capabilities, real-time monitoring, and automated expert-level analysis at the patient’s bedside. This comprehensive examination explores the integration of advanced machine learning algorithms with POC technologies, highlighting their potential to revolutionize cardiovascular disease detection, monitoring, and management. Recent breakthroughs in deep learning frameworks, particularly those that emulate neural processes, have enabled more frequent detection of cardiovascular abnormalities and provided automated diagnostic capabilities comparable to expert clinicians. However, significant challenges persist, including data privacy concerns, algorithmic bias, and the need for robust clinical validation. Despite these barriers, the translational potential of machine learning-assisted POC devices presents unprecedented opportunities for improving cardiovascular health outcomes, particularly in resource-limited settings where traditional diagnostic infrastructure is insufficient. This work synthesizes current developments, analyzes emerging technologies, and provides a roadmap for future innovation in this rapidly evolving field.