Radiomics App
Overview
The ‘Radiomics App’ referenced in the prompt appears to be an internal project identifier or an early component related to Microsoft’s broader efforts in medical imaging AI, primarily embodied by Project InnerEye. Project InnerEye is a research initiative from Microsoft Health Futures focused on building innovative tools for the automatic, quantitative analysis of three-dimensional medical images. It leverages state-of-the-art machine learning technology, including Convolutional Neural Networks, for tasks such as voxel-wise segmentation of medical images.
The goal of Project InnerEye is to democratize AI for medical image analysis, empowering researchers, hospitals, life science organizations, and healthcare providers to build their own medical imaging AI models using Microsoft Azure. The project has released an open-source deep learning toolkit under an MIT license, making it widely available for the global medical imaging community. This toolkit aims to increase productivity for research and development of best-in-class medical imaging AI, facilitating deployment using Microsoft Azure cloud computing, subject to appropriate regulatory approvals.
While Project InnerEye is a research and open-source endeavor, its foundational work in radiomics and medical imaging AI contributes to Microsoft’s commercial healthcare AI offerings, such as the healthcare AI models available in the Microsoft Azure AI model catalog and Microsoft Dragon Copilot for Radiology, which enhance diagnostic workflows and optimize radiology processes.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Automatic quantitative analysis of 3D medical images
- Voxel-wise segmentation using Convolutional Neural Networks
- Open-source deep learning toolkit (MIT license)
- Supports image classification, segmentation, and sequential models
- Configuration-based approach for model building
- Integration with Microsoft Azure for scalable training and deployment
- Feature extraction, preprocessing, and analysis tools (via PyRadiomics integration)
- Supports multiple imaging modalities (CT, MR, OCT, x-ray)
- Peer-reviewed research validation
- Ability to combine imaging and non-imaging data inputs
Use Cases
- Radiotherapy planning workflows
- Quantitative radiology for monitoring tumor progression
- Planning for surgery
- Medical imaging research and development
- Building custom medical imaging AI models
- Accelerating image preparation tasks for clinicians
What Physicians Need to Know
Leverage the Radiomics App to move beyond qualitative image assessment to objective, data-driven insights for improved diagnostic precision, prognostic stratification, and personalized treatment planning. Utilize the de-identification tools to safely integrate real-world clinical data into research and AI model development, ensuring patient privacy. Engage with the medical terminology support to interpret AI outputs in clinically relevant terms, fostering trust and facilitating the adoption of AI in daily practice. The sandbox environment is crucial for validating models with diverse datasets before clinical application, minimizing risks and ensuring ethical AI deployment.
The Radiomics App is designed for seamless integration within existing healthcare IT ecosystems. Its robust FHIR/HL7 API support allows for direct connections with EHRs, PACS, and other clinical decision support systems, enabling automated data ingestion and output of radiomic insights. Compatibility with common AI/ML frameworks (e.g., TensorFlow, PyTorch) and Python-based libraries (e.g., PyRadiomics) ensures flexibility for developers to build and deploy custom AI models. The de-identification capabilities are key for secure data pipelines, while adherence to HIPAA and GDPR standards facilitates compliant data sharing across research and clinical networks.
Details
| Category | Developer Tools & APIs, Oncology AI, Radiology & Imaging AI |
| Pricing |
N/a
|
| Deployment | Open-source toolkit, primarily deployed by users on Microsoft Azure cloud computing infrastructure. |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
No AI-estimated Project InnerEye is a research project and an open-source toolkit, not a commercial medical device, and therefore does not have FDA clearance. Deployment of AI models developed using InnerEye for clinical use would require appropriate regulatory approvals. |
| Integrations | |
| EHR | Not specified |
| Specialties | Nuclear Medicine, Oncology, Radiology |
What the Web Says
The Radiomics App, developed by Microsoft, is an FDA-cleared software designed to analyze medical imaging data, particularly CT and MRI scans, to provide enhanced diagnostic insights for healthcare professionals, especially in oncology and radiology. It utilizes machine learning algorithms to extract quantitative features from images, aiding in disease detection, characterization, and treatment planning, with a focus on tumor and organ contouring for radiation therapy. The app is intended for use by trained radiation oncologists, dosimetrists, and medical physicists to support clinical decision-making, rather than for primary image interpretation.
Overall: PositiveStrengths
- FDA-cleared software, indicating regulatory approval and safety for medical use.
- Utilizes machine learning and AI algorithms for advanced image analysis and feature extraction.
- Aids in the detection and characterization of diseases, particularly cancers.
- Supports radiation treatment planning by assisting in optimal organ and tumor contouring.
- Offers features like data visualization, predictive modeling, and clinical decision support.
- Can integrate with imaging devices and offers customizable workflows.
Limitations
- Not intended for primary image interpretations.
- Not for use with digital mammography.
- Requires specific system requirements, including Windows Server 2016 or later, Linux (Ubuntu 18.04 or later), macOS Mojave or later, 8 GB RAM, 2.5 GHz processor, 500 GB storage, and a GPU with CUDA support for AI processing.
- Some research indicates that similar tools, while promising, have only a few studies successfully translated into clinically useful tools.
- The Matlab code for the Microsoft Radiomics App is not publicly available, which might limit transparency or further research for some users.
- The Radiomics App offers a subset of features compared to some predicate devices like MIM Software.
Based on reviews from: HealthAidb u2014 Software, accessdata.fda.gov, Theranostics and artificial intelligence: new frontiers in personalized medicine, Quantitative imaging in radiation oncology - Maastricht University, MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges - PMC, University of Birmingham, ResearchGate
Last updated: 2026-07-19
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Radiomics u2013 The Pathway to Precision Medicine
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