Enterprise AI Analysis
MOSAIC: Multilingual, Taxonomy-Agnostic AI for Radiology
The MOSAIC framework revolutionizes radiological report classification by addressing critical limitations of current AI methods. It offers a multilingual, adaptable, and computationally efficient solution, enabling accurate insights from diverse imaging data with minimal annotation effort.
Accelerate Clinical Insights & Reduce Annotation Costs
MOSAIC empowers healthcare enterprises to leverage AI for radiological report analysis, significantly reducing the dependency on costly manual annotations and overcoming language barriers. Its design prioritizes practical deployment and rapid adaptation.
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Mean Macro F1 Score (Chest X-Ray)
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GPU Memory for Deployment
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Annotated Samples for 82% F1 (Danish)
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Expert-Level Performance Achieved
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Breaking Language Barriers in Radiology
MOSAIC is inherently multilingual, trained and evaluated across English, Spanish, French, and Danish. This capability is crucial for global healthcare applications, where reports are generated in various languages. The model demonstrates strong language competency, ensuring high performance regardless of the input language, a significant advantage over English-centric LLMs. Finding 3 highlights that adding more languages and datasets, especially in a full multilingual setting, significantly improves performance, with English-only training showing transferability to Spanish due to shared clinical structure.
Efficient AI for On-Premise Clinical Use
Designed for small-scale deployment, MOSAIC operates efficiently on consumer-grade GPUs (e.g., NVIDIA RTX 4090 with 24GB memory). This eliminates reliance on costly, closed-source, or resource-intensive models that raise privacy concerns in clinical settings. The use of compact, open-access language models like MedGemma-4B allows for local development and adaptation, ensuring data privacy and operational autonomy.
Rapid Adaptation to New Taxonomies & Modalities
MOSAIC is robust to variations in label taxonomies and medical imaging modalities. It supports both zero-shot and few-shot prompting, and lightweight fine-tuning, reducing the need for extensive manual annotation. Findings 2 and 4 emphasize that in-context examples and supervised fine-tuning consistently improve performance, and fine-tuning enhances generalization to unseen datasets and taxonomies. Finding 8 further shows that cross-imaging modality transfer (e.g., from Chest X-ray to Brain MRI) improves performance, especially with English data augmentation.
88%
Mean Macro F1 Score for Chest X-Ray Classification
MOSAIC achieves a high classification accuracy across diverse chest X-ray datasets, demonstrating its reliability in a critical diagnostic domain.
| Feature | MOSAIC Advantage | Traditional Methods (Rule-Based, BERT) | Proprietary LLMs (GPT-4, Llama-70B) |
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| Multilingual Support |
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Enterprise Process Flow
Diverse Radiology Reports (Multilingual)
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Compact MedGemma-4B LLM
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Multilingual Fine-Tuning
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Zero/Few-Shot Classification
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Consumer-Grade GPU Deployment
Case Study: Rapid Adaptation with Minimal Data
MOSAIC's data augmentation capabilities enable highly efficient domain adaptation. On Danish reports (DanskCXR), initializing MOSAIC from a pre-trained multilingual model (MPE+SC) and fine-tuning with just 5% of the dataset (80 annotated examples) achieves a weighted F1 score of 82. This result is remarkably close to the 86% achieved with the full 1600-sample training set, demonstrating significant reductions in annotation effort. This approach is particularly powerful for adapting to new languages or medical imaging modalities with limited expert-labeled data.
Key Outcome: 80 Annotated Samples for Near Full Performance (DanskCXR)
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your organization could realize by integrating MOSAIC's advanced AI capabilities.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to AI-Powered Radiology
Our structured implementation roadmap ensures a seamless integration of MOSAIC into your existing workflows, maximizing impact with minimal disruption.
Discovery & Strategy
Initial consultation to understand your specific needs, data landscape, and define clear objectives for AI integration. Identify target languages and taxonomies.
Data Preparation & Model Adaptation
Securely collect and prepare relevant radiological reports. Fine-tune MOSAIC on a small, representative dataset using few-shot learning or data augmentation for rapid adaptation.
Integration & Deployment
Deploy MOSAIC locally on your consumer-grade GPU infrastructure. Integrate the classification outputs with your existing PACS or EMR systems.
Validation & Optimization
Validate model performance against clinical benchmarks. Continuously monitor and optimize for evolving needs and new data distributions.
Ready to Transform Your Radiology Workflow?
Embrace the future of medical imaging analysis with MOSAIC. Schedule a personalized consultation to explore how our multilingual, efficient, and adaptable AI can benefit your enterprise.
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