Gleamer
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About
Gleamer is an advanced, unified AI platform designed specifically for medical imaging and radiology. It serves as a comprehensive AI Copilot that integrates directly into clinical workflows to assist doctors in reading scans faster and with higher confidence. The platform, built on GleamerOS, provides a single, cohesive design and user experience across various medical modalities, moving away from fragmented AI tools to a centralized diagnostic ecosystem. This approach aims to reduce the cognitive load on radiologists while improving the overall quality of care in clinical environments. The suite of tools covers a wide range of imaging needs. For X-rays, it includes BoneView for fracture detection, ChestView for thoracic abnormalities, and BoneAge for pediatric assessments. In the realm of CT scans, LungCT and BoneCT offer automated analysis, while the MRI segment features Pixyl.Neuro for neurological conditions such as Multiple Sclerosis. Beyond image analysis, Gleamer offers Reporting AI features like Voice and AutoReport, which streamline the transcription and generation of clinical reports. These tools work in tandem to capture findings automatically and translate them into structured documentation. Gleamer is primarily built for radiologists, hospital networks, and emergency departments where rapid and accurate interpretation of scans is critical. For IT teams, the GleamerOS provides a unified integration point, simplifying the deployment of multiple AI models within a hospital's existing infrastructure. By consolidating various AI functions—from bone fracture detection to neurological MRI analysis—into one interface, the platform addresses the challenge of software fragmentation in modern medical facilities. This makes it an ideal choice for institutions looking to scale their AI capabilities without complicating their technical stack. What sets Gleamer apart is its unified approach and commitment to clinical evidence. Rather than acting as a series of disconnected plugins, it functions as a comprehensive operating system for radiology AI. This "One platform, one design" philosophy ensures that medical professionals do not have to learn multiple interfaces, which is crucial in high-stress medical settings. Furthermore, its dedication to clinical studies and evidence-based AI development provides a level of medical-grade reliability that distinguishes it from newer or less-vetted diagnostic tools on the market.
Pros & Cons
Provides a unified AI platform (GleamerOS) to avoid fragmented software workflows
Offers clinically validated AI models backed by extensive published medical studies
Supports a wide variety of modalities including X-ray, CT, MRI, and MMG
Reduces reporting time through automated transcription and AutoReport features
Designed specifically to improve diagnostic confidence in high-pressure emergency departments
Pricing information is not publicly available and requires a custom demo request
The platform is highly specialized for medical professionals and not for general users
Full functionality requires deep integration with specific hospital IT infrastructures
Use Cases
Radiologists can use automated tools like BoneView to detect fractures faster, reducing the risk of oversight during long shifts.
IT Administrators in hospitals can streamline their AI stack by using GleamerOS as a single point of integration for multiple diagnostic models.
Emergency department doctors can utilize the AI copilot for immediate preliminary scan readings to help prioritize urgent patient cases.
Medical facility managers can implement AutoReport to increase the volume of scans processed while maintaining documentation standards.
Platform
Features
• voice-to-text reporting
• boneage pediatric assessment
• gleameros unified platform
• autoreport generation
• pixyl.neuro mri analysis
• lungct automated scanning
• chestview thoracic analysis
• boneview fracture detection
FAQs
What clinical areas does Gleamer cover?
Gleamer provides AI solutions across several modalities including X-ray (bone and chest), CT (lung and bone), MRI (neuro and lumbar), and Mammography (BreastView). It also includes specialized tools like BoneAge for pediatric radiology.
How does Gleamer integrate into existing hospital workflows?
The platform uses GleamerOS to provide a unified experience, integrating reporting AI and diagnostic tools directly into the radiologist's current environment. This minimizes the need to switch between different software applications.
Is Gleamer's AI clinically validated?
Yes, Gleamer emphasizes evidence-based AI and maintains a dedicated clinical studies section to demonstrate the accuracy and reliability of its diagnostic models in real-world medical settings.
What is AutoReport and how does it help?
AutoReport is an AI-driven tool that automatically generates structured reports based on imaging analysis findings. This significantly reduces the manual data entry required by radiologists, allowing them to focus on complex diagnosis.
Pricing Plans
Custom Demo
Unknown Price• Access to Gleamer Copilot suite
• Clinical workflow integration
• Support for X-ray, CT, and MRI modalities
• Automated reporting tools (AutoReport)
• GleamerOS unified platform access
• Clinically validated AI insights
• IT support for hospital deployment
Job Opportunities
AI Engineer (H/F)
Improve diagnostic precision and clinical workflow efficiency with an AI-driven radiology platform that automates X-ray, CT, and MRI analysis for medical teams.
Education Requirements:
PhD or more
Experience Requirements:
> 3 years
Other Requirements:
Strong ML foundations (probability, linear algebra, optimization)
Proficient in Python + PyTorch
Strong communication and scientific rigor
Motivation to make a real impact in healthcare
Responsibilities:
Design and scale visual representations from large de-identified 2D/3D datasets
Adapt and optimize models powering Gleamer’s clinical features
Fine-tune encoders + LLMs for report generation and extraction
Inference optimization (Docker, ONNX, TensorRT, batching)
Contribute across pretraining and fine-tuning stacks
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Member of technical staff (Inference)
Improve diagnostic precision and clinical workflow efficiency with an AI-driven radiology platform that automates X-ray, CT, and MRI analysis for medical teams.
Benefits:
Stock purchase plan / Stock options
Free food & snacks
Afterworks, Team lunches
Partners with Nonprofits
Competitive salary
Education Requirements:
MS or PhD in Computer Science, Machine Learning or related fields
Other Requirements:
Proficient in Python, Rust or C/C++
Experience in GPU programming (CUDA, Open AI Triton, Metal)
Experience in model compression and quantization techniques
Collaborative mindset
Strong communication and presentation skills
Responsibilities:
Develop scalable, low-latency and cost effective inference pipelines
Optimize model performance (memory usage, throughput, and latency)
Develop specialized GPU kernels for performance-critical tasks
Collaborate with research teams on model architectures
Implement state-of-the-art inference techniques
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AI Engineer
Improve diagnostic precision and clinical workflow efficiency with an AI-driven radiology platform that automates X-ray, CT, and MRI analysis for medical teams.
Benefits:
Flexible working hours
Food service or stocked kitchen
Stock purchase plan / Stock options
Team building
Afterworks, Team lunches
Other Requirements:
Fast-moving, product-minded full-stack engineer
Autonomous and drive projects to completion
Tech stack: Python, OpenAI API, Pytorch, Hugging Face, PostgreSQL, GCP, Poetry
Forward-thinking regarding AI potential
Responsibilities:
Design and implement end-to-end AI features for HR agents
Test, fine-tune, and evaluate cutting-edge models
Build frameworks to measure accuracy, reliability, and bias
Work on latency, caching, and cost-optimization strategies
Ensure AI features meet enterprise standards for privacy and fairness
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