Wetter's Digital Twin: A Patient-Specific Framework for Artificial Intelligence and Healthcare

The concept of ORReady began with a simple goal: to better prepare the patient, surgeon, operating team, and operating room (OR) environment to improve surgical outcomes. Advances in artificial intelligence, large datasets, electronic health records, imaging, physiological monitoring, wearable technologies, and predictive analytics now create an opportunity to take that concept considerably further. Digital twins, originally developed in engineering, use evolving virtual representations of physical systems to model performance and test possible interventions before they are applied. We propose Wetter's Digital Twin (WDT) as an individualized, dynamic model of the patient that integrates personal health information with artificial intelligence, medical knowledge, and relevant population data. Unlike the conventional medical record, which primarily documents what has occurred, WDT would help clinicians understand what may happen next and explore how different interventions might alter that course. In surgery, such a model could assist with preoperative optimization, procedure selection and planning, intraoperative decision support, and postoperative recovery. The concept, however, extends beyond surgery. The same longitudinal patient-specific model could contribute to prevention, diagnosis, medical treatment, chronic disease management, rehabilitation, and long-term health planning. WDT is proposed not as a replacement for physician judgment or patient participation, but as a means of making increasingly complex health information more useful to both. Important challenges involving data integration, validation, privacy, security, bias, regulation, and clinical responsibility remain. The transition from a medical record that primarily records the past toward a patient-specific model that can also evaluate possible futures may represent an important next step in healthcare.