The Decision Twin: Metaverse Patient Digital Twins as Executable Clinical Reality
TL;DR
This work introduces the Metaverse Patient Digital Twin as a decision-grade clinical artifact defined by one requirement: every displayed claim or simulated scenario must be traceable to a versioned patient state, explicit assumptions, and replayable interaction logs.
Abstract
Clinical AI is no longer bottlenecked only by model performance; it is bottlenecked by the accountable interaction loop through which clinicians and patients inspect evidence, test alternatives, and remain responsible for decisions under uncertainty. We argue that today's digital-twin systems, XR interfaces, and foundation-model copilots each address part of this loop, but they fail when deployed as separate products: predictions are not replayable across time, XR becomes descriptive visualization without executable state, and copilots are fluent without auditable grounding. We introduce the Metaverse Patient Digital Twin (MPDT) as a decision-grade clinical artifact defined by one requirement: every displayed claim or simulated scenario must be traceable to a versioned patient state, explicit assumptions, and replayable interaction logs. We specify minimal acceptance criteria, a reference loop in which stakeholders observe new evidence, update the twin state, run bounded simulations, generate explanations, commit decisions, and then log and monitor outcomes, along with a compact architecture that binds interoperability, simulation, governed interaction, and lifecycle controls. Finally, we outline workflows (risk stratification, diagnosis support, treatment rehearsal, training, cross-site coordination) and the evidence required to make MPDTs defensible: calibration over time, subgroup reliability, category-error prevention (observation vs simulation), and measurable workflow outcomes.