# EXR: An Interactive Immersive EHR Visualization in Extended Reality

arXiv: https://arxiv.org/abs/2512.05438 · HTML: https://mlsanity.com/paper/2512.05438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records](https://mlsanity.com/paper/2401.07128/) (2024)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Iterative fully convolutional neural networks for automatic vertebra segmentation and identification](https://mlsanity.com/paper/1804.04383/) (2018)
- [Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data](https://mlsanity.com/paper/2210.13462/) (2022)
- [SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation](https://mlsanity.com/paper/2402.16368/) (2024)
- [A Comprehensive Review of Multimodal XR Applications, Risks, and Ethical Challenges in the Metaverse](https://mlsanity.com/paper/2411.04508/) (2024)
- [AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2404.00122/) (2024)

## Abstract (arXiv)

This paper presents the design and implementation of an Extended Reality (XR) platform for immersive, interactive visualization of Electronic Health Records (EHRs). The system extends beyond conventional 2D interfaces by visualizing both structured and unstructured patient data into a shared 3D environment, enabling intuitive exploration and real-time collaboration. The modular infrastructure integrates FHIR-based EHR data with volumetric medical imaging and AI-generated segmentation, ensuring interoperability with modern healthcare systems. The platform's capabilities are demonstrated using synthetic EHR datasets and computed tomography (CT)-derived spine models processed through an AI-powered segmentation pipeline. This work suggests that such integrated XR solutions could form the foundation for next-generation clinical decision-support tools, where advanced data infrastructures are directly accessible in an interactive and spatially rich environment.