Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework (Global Journal of Sciences, 2025)
The convergence of digital twin technology and machine learning has ushered in a transformative era in patient monitoring and diagnosis within the healthcare sector. This review article explores the comprehensive integration of digital twin-driven machine learning frameworks, aiming to elucidate the core objectives, pivotal findings, and overarching implications. Our primary objectives encompass the exploration of digital twin technology's adaptation to healthcare, the augmentation of medical assessments through machine learning algorithms, the enabling of real time monitoring with early anomaly detection capabilities, and the personalization of treatment plans rooted in patient profiles generated by digital twins. The key findings underscore the successful adaptation of digital twin technology for healthcare applications, emphasizing its potential to capture dynamic patient data and history. The synergy between machine learning and digital twins enhances the precision of diagnostics and predictive analytics, thus improving healthcare outcomes. Real-time monitoring, made possible through digital twins, ensures proactive patient care with timely interventions. Moreover, personalizing treatment plans, tailored to individual patient profiles, offers a promising avenue for more effective and less invasive interventions. The implications of this review extend to the transformative potential of digital twin driven machine learning in healthcare, with the ability to revolutionize patient care, diagnostics, and monitoring. The review highlights data security and ethical challenges, stressing the need for standardized protocols to protect patient information. Ongoing research and innovation are crucial for maximizing these frameworks' potential, improving patient outcomes, and enhancing healthcare quality.
Keywords: time monitoring; augmentation; medical assessments; patient data and history