Digital Twins in Anesthesia and Critical Care

Digital twins are virtual representations of real-world objects or systems that can be used to model and predict their behavior in response to various scenarios. Digital twins are becoming increasingly relevant in planning medical care, e.g. , in neurology, cardiology, and oncology. They also hold significant promise for advancing anesthesia and critical care medicine by tailoring interventions to a patient’s physiological data, imaging, monitoring streams, and clinical history rather than relying on population averages, ultimately helping to enhance clinical decision‑making and improve health outcomes. However, their adoption raises concerns over data privacy, dependency on technology, and cost. This article explores the concept of digital twins and their potential applications, benefits, and challenges within the fields of anesthesia and critical care medicine.