Digital Twins in Surgery as a Real-Time Decision Support System: A Multi-Layered Framework

Surgical care requires real-time coordination among patients, clinicians, devices, and data, often under unpredictable and time-critical conditions. Yet, current hospital systems struggle with adaptability and integration, limiting their ability to respond dynamically during surgery. Digital Twin (DT) technology, initially developed in aerospace and manufacturing, is now emerging as a promising tool for enhancing situational awareness and supporting real-time clinical decisions. This paper presents a multi-layered DT Decision Support System (DT-DSS) architecture tailored to the surgical domain. Drawing from recent literature and scoping reviews, we identify current limitations in model fidelity, system integration, and ethical transparency. We propose a modular framework that spans data ingestion, simulation, predictive optimization, clinical decision-making, and governance. Each architectural layer is backed by validated studies, offering a practical and trustworthy pathway toward real-world surgical implementation.