 One of the important problems in contemporary medicine is how to account for patient-specific variation. Systems medicine is sometimes highlighted as a route to personalized or precision medicine (Hood et al. 2015). The aim to personalize medicine is as old as the medical profession itself, but it is now promoted through data-intensive computational strategies with exciting new potentials (Tutton 2014). Proponents are optimistic that large-scale modeling of patient-specific data can result in a paradigm-shift from a one-size-fits-all model in traditional medicine, toward a more efficient systemic approach that can tailor disease prevention, diagnosis, and treatment to individual patients. Against this optimism, some have raised concerns about uncertainties and potential harmful results associated with genetic risk profiling, including the risk of overdiagnosis and unnecessary medicalization of healthy individuals (Green &amp; Vogt 2016; Vogt et al. 2016, 2019). Philosophers and social scientists have also critically examined how risk information is communicated to the public by companies capitalizing on personalized genomics, and discussed how such information may affect health outcomes and perceptions of health (Bartol 2013; Prainsack 2017; Reydon et al. 2012). More generally, systems medicine has spurred debates about whether the vision to predict and control future health outcomes is compatible with a humanistic approach to medicine and social complexity (Tutton 2014; Vogt et al. 2014). Moreover, combination of systems medicine and synthetic biology applications opens also for what O’Malley (2011a) termed constructive personalized medicine, where predictive modeling and synthetic interventions are combined. Promising technologies for these purposes now include CRISPR-Cas9 gene editing, organ-on-chip models, 3D bioprinting and organoid-based models for replacement therapies. While creating unprecedented potentials for disease treatment, they also raise ethical questions about artificial chimerism and human enhancement (Pio-Lopez 2021).