Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine

Big data and Al have enabled digital simulation for prediction of future health states or behaviors of specific individuals, populations or humans in general. Digital simulacra use multimodal datasets to develop computational models that are virtual representations of people or groups, generating predictions of how systems evolve and react to interventions over time. These include digital twins and virtual patients for in silico clinical trials, both of which seek to transform research and health care by speeding innovation and bridging the epistemic gap between population-based research findings and their application to the individual. Nevertheless, digital simulacra mark a major milestone on a trajectory to embrace the epistemic culture of data science and a potential abandonment of medical epistemological concepts of causality and representation. In doing so, data first' approaches potentially shift moral attention from actual patients and principles, such as equity, to simulated patients and patient data.

Automated summary

Big data and Al enable digital simulation for predicting future health states or behaviors. These digital simulacra use multimodal datasets to generate virtual representations of individuals or groups, predicting the evolution and interventions of systems over time. While it speeds up innovation and bridges the gap between research findings and individual applications, it may also lead to the abandonment of causality and representation concepts in medical epistemology, shifting the focus from actual patients to simulated patients and patient data.