ArticleInternational journal of computer assisted radiology and surgery2025
Intelligent control of robotic X-ray devices using a language-promptable digital twin.
Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- FluoroSAM: A Language-promptable Foundation Model for Flexible X-ray Image Segmentation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Mini-review on human-centered assurance in robot-assisted orthopedics and neurosurgery.Frontiers in robotics and AI · 2026Review
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Authors and funding
6 authors.
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Abstract
purposeNatural language offers a convenient, flexible interface for controlling robotic C-arm X-ray systems, making advanced functionality and controls easily accessible.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Benjamin D.] Last name [Killeen]. Also, kindly confirm the details in the metadata are correct. However, enabling language interfaces requires specialized artificial intelligence (AI) models that interpret X-ray images to create a semantic representation for language-based reasoning. The fixed outputs of such AI models fundamentally limits the functionality of language controls that users may access. Incorporating flexible and language-aligned AI models that can be prompted through language control facilitates more flexible interfaces for a much wider variety of tasks and procedures.
methodsUsing a language-aligned foundation model for X-ray image segmentation, our system continually updates a patient digital twin based on sparse reconstructions of desired anatomical structures. This allows for multiple autonomous capabilities, including visualization, patient-specific viewfinding, and automatic collimation from novel viewpoints, enabling complex language control commands like "Focus in on the lower lumbar vertebrae."
resultsIn a cadaver study, multiple users were able to visualize, localize, and collimate around structures across the torso region using only verbal commands to control a robotic X-ray system, with 84% end-to-end success. In post hoc analysis of randomly oriented images, our patient digital twin was able to localize 35 commonly requested structures from a given image to within
conclusionOverall, we show how intelligent robotic X-ray systems can incorporate physicians' expressed intent directly. Existing foundation models for intra-operative X-ray image analysis exhibit certain failure modes. Nevertheless, our results suggest that as these models become more capable, they can facilitate highly flexible, intelligent robotic C-arms.
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