Revolutionizing Your Radiology Workflows with AI in Medical Imaging
Streamline image processing and uncover hidden anomalies 10× faster without costly GPUs.
Featured by Leading Institutions
What is RCL®
Random Contrast Learning (RCL®) is our patent-pending machine learning algorithm designed to redefine how models learn from data. Unlike traditional neural networks, RCL® focuses on the differences between data points, enabling powerful generalization with minimal compute resources. It is an ML technique designed to enhance classification tasks by reducing the need for extensive data, time, energy, and hardware resources while improving accuracy.
Innovative Contrast-Driven Learning
RCL® analyzes subtle differences across samples to reveal insights that traditional models miss—delivering accurate predictions with minimal data.
Unmatched Speed & Efficiency
Train on any CPU—up to 25× faster than conventional AI—so you get actionable results in minutes, not days, without extra hardware.
Effortless Integration & Real-World Impact
Deploy RCL® with one line of code or a click in our UI. Seamlessly scale from pilot to production and solve real-world challenges instantly.
How RCL® Works – In 3 Steps
Upload Your Data
Drag & drop DICOM, PNG, or CSV files (X-rays, CT, MRI, tabular data) into our secure interface.
Run RCL® Training
Execute one line of Python—or click “Train” in our UI—to initiate Random Contrast Learning.
Get Instant Results
View classification and anomaly-detection outputs locally, with no GPU or cloud dependency.
Real-World Use Cases in Medical Imaging
X-ray
RCL® improves X-ray analysis with AI that supports mammography (breast cancer imaging) by accurately distinguishing benign from malignant breast lesions, aiding early detection and improving patient outcomes.
CT Scan
Utilize advanced algorithms to analyze CT scans, identifying subtle lung nodules, vascular issues, and other abnormalities to support timely and accurate diagnoses.
MRI Scan
Transform MRI interpretation by enhancing soft-tissue contrast, enabling precise detection of brain tumors and other critical abnormalities for improved diagnoses.
Ultrasound
Leverage AI-driven insights in ultrasound imaging to assess fetal development, abdominal organs, and cardiac function, facilitating rapid and reliable clinical evaluations.
Transforming Radiology Today
Discover how AI is revolutionizing medical imaging, enhancing efficiency, and improving patient outcomes.
Billion Dollars
Projected AI in medical imaging market size by 2033. Source: GlobeNewswire, 2024
Minutes Saved per Shift
Pilot testing of RCL® technology demonstrated an average reduction of 60 minutes per radiologist shift, significantly enhancing workflow efficiency.
%
Burnout Rate
Nearly half of private practice radiologists report experiencing burnout.
Source: Journal of the American College of Radiology, 2023
Why Choose RCL® for Radiology
RCL® (Random Contrast Learning) makes AI in Radiology finally practical—delivering GPU-free, one-line CPU training for high-precision diagnostics.
Works on existing CPUs
No need for GPUs or cloud—runs on your hospital’s existing systems.
25x Faster Model Training
Train models in minutes, not weeks. Rapid deployment made simple.
100% On-Premise Option
Keep sensitive data in-house. Full compliance with HIPAA and IT policies.
See RCL® in Action – Customized to Your Workflow
- How it integrates with PACS
- Hardware Requirements: Just a CPU
- Explore your own imaging data in a guided demo
Our Solutions
PrismRCL (Flexible Deployment)
PrismRCL adapts to your infrastructure needs, whether on-premises or in the cloud. Its containerized design ensures seamless integration while providing you with complete control over your data and security. No need for high-end GPUs – get enterprise-grade AI on your existing CPUs.
PrismRCL for Windows
Leverage the power of AI directly on your existing hardware with PrismRCL for Windows. Train models efficiently without relying on external servers, keeping your workflow fast, secure, and under your control. Download Now
Frequently Asked Questions
Everything you need to know about Lumina AI:
How does AI enhance accuracy in medical imaging?
AI technologies, such as Random Contrast Learning (RCL®), enhance accuracy in medical imaging by identifying subtle patterns and anomalies that are often difficult to detect manually. By efficiently analyzing large volumes of imaging data, RCL supports radiologists in accurately diagnosing conditions, such as cancers, earlier, leading to improved patient outcomes.
Can AI assist in diagnosing rare diseases?
AI models analyze complex medical data, aiding in the early detection of rare diseases through imaging and predictive analytics.
How does Lumina AI integrate into existing hospital workflows?
Lumina AI’s solutions can easily integrate with existing hospital IT infrastructure on standard CPUs, assisting in diagnostics, decision support, and predictive analytics.
What makes RCL® different from traditional AI models?
Random Contrast Learning (RCL™) enables faster model training with minimal data requirements, improving efficiency without requiring external high-end GPUs.
Can AI help in detecting breast cancer?
AI models, including RCL™, analyze mammograms to differentiate between benign and malignant tumors, improving early detection rates.
How secure is AI-driven medical imaging for patient data?
AI solutions follow strict compliance protocols, ensuring patient data privacy through encryption and federated learning techniques.
What are the key AI applications in radiology?
AI is used for automated anomaly detection in X-rays, MRIs, and CT scans, enabling early diagnosis and reducing the manual workload of radiologists.