The Role of Digital Twins in Personalizing Patient Treatment Plans in Healthcare

Digital twins, virtual replicas of physical entities or processes, have emerged as transformative tools in healthcare, offering unprecedented opportunities for personalizing patient treatment plans. By integrating real-time patient data with predictive models, digital twins enable healthcare providers to simulate outcomes, optimize interventions, and align treatment strategies with individual patient needs. This paper investigates the foundational concept of digital twins, their benefits in creating personalized treatment plans, and the technological infrastructure required for their implementation. It also examines ethical challenges and governance issues related to patient data, algorithmic biases, and decision-making. Furthermore, the discussion highlights future opportunities, including the integration of AI and machine learning to enhance digital twin systems. By addressing current limitations and ethical considerations, digital twins have the potential to revolutionize healthcare delivery, reduce costs, and improve patient outcomes on a global scale. Keywords: Digital twins, personalized medicine, healthcare technology, predictive modeling, real-time patient data.