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arXiv · 2608.21416

Operational digital twin clinics enable task-based evaluation of embodied AI

Xinyuan Wu·Jingrao Zhang·Mengdi Xu·Henry K. Chu·Mingguang He·Danli Shi
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Abstract

Embodied artificial intelligence (AI) must be tested in the clinical environments where it will operate, but building realistic, robot-testable settings is costly and difficult to scale. Here we show that routine clinic images can be transformed into operational digital twins for task-based evaluation of embodied AI. Using 39 ophthalmic clinic scenes, we converted single photographs into editable, simulator-ready environments and assessed reconstruction quality, room-scale geometry, mesh grounding, multi-robot feasibility, perturbation sensitivity and closed-loop policy performance. The reconstructed scenes preserved workspace structure, while local editing enabled controlled device reconfiguration. Device meshes, collision proxies and semantic anchors converted visual reconstructions into contact-aware simulation scenes. Across three robot embodiments, shared task targets showed different patterns of reachability and contact feasibility. Small device translations and rotations produced task-specific changes in contact margins that were not captured by visual similarity alone. Digital-twin trajectories also supported local policy learning and closed-loop evaluation. These findings establish operational validity as a key principle for clinical digital twins and provide an intermediate layer between offline development and physical deployment of embodied AI in healthcare.

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Xinyuan Wu, Jingrao Zhang, Mengdi Xu, Henry K. Chu, Mingguang He, Danli Shi. 2026-08-12. Operational digital twin clinics enable task-based evaluation of embodied AI. https://arxiv.org/abs/2608.21416

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