Towards the future of personalized medicine: digital twin technology

Digital twin technology is emerging as a transformative paradigm in healthcare, shifting practice from provider-centered models toward more personalized forms of medicine. As dynamic virtual representations of the human body, digital twins integrate biometric data, lifestyle patterns, and clinical records to simulate, monitor, and predict health trajectories in real time. Their growing use raises not only technical possibilities but also important questions about how patients relate to these data-driven counterparts, particularly when twins inform everyday health decisions in chronic care, such as diabetes or oncology. This perspective examines these relational dynamics and their ethical, cultural, and experiential implications for autonomy, decision-making, and the lived experience of being represented in data. To guide this analysis, we introduce a scale framework with three intersecting lenses: time, distinguishing asynchronous from synchronous updating; twining, ranging from close mirroring to more augmentative forms of representation; and control, spanning human-led to twin-driven decision authority. Using this framework, we position four common types of digital twins: mirror, shadow, intelligent, and simulacra as an evolution from basic representation to transformative modeling. We argue that future healthcare and public health policy must go beyond technical innovation to address patients’ lived experiences, ensuring that digital twins enhance rather than diminish autonomy, trust, and equity. This perspective thus calls for a patient-centered approach in designing and implementing digital twin technologies.