Evidence map›Paper›PMID 40205315›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Intelligent control of robotic X-ray devices using a language-promptable digital twin.

Benjamin D Killeen, Anushri Suresh, Catalina Gomez, Blanca Íñigo, Christopher Bailey, Mathias Unberath

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. 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 · 2026
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Benjamin D KilleenLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, 21218, MD, USA. killeen@jhu.edu.ORCID http://orcid.org/0000-0003-2511-7929
Anushri SureshLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, 21218, MD, USA.
Catalina GomezLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, 21218, MD, USA.
Blanca ÍñigoLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, 21218, MD, USA.
Christopher BaileyDepartment of Interventional Radiology, Johns Hopkins University, Baltimore, 212187, MD, USA.
Mathias UnberathLaboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, 21218, MD, USA.

Funding

A Multi-robot System for Semi-automated Image-guided Vertebral AugmentationR01EB036341 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI Mathias Unberath · 2024 to 2026
$1.5M
Foundation for the National Institutes of Health R01EB036341National Science Foundation 2239077NIBIB NIH HHS R01 EB036341
6 · The paper itself

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.

Indexed as

Artificial IntelligenceNatural Language ProcessingRoboticsHumansFluoroscopyFoundation modelsImage-guided surgeryLarge language modelsSegment AnythingVoice user interfaces

Identifiers

PMID40205315
PMCPMC12915047

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.