Limbix (by BigHealth)
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Accelerate clinical AI workflows with AI-assisted annotation and secure federated learning.
NVIDIA Clara Train SDK is a domain-specific application framework for medical imaging, now largely evolved and integrated into the MONAI (Medical Open Network for AI) ecosystem. As of 2026, it serves as the enterprise-grade foundation for clinical AI model development, specifically targeting radiologists and data scientists who require accelerated annotation and robust training pipelines. The architecture leverages NVIDIA's CUDA-X stack, providing high-performance primitives for 3D medical image processing. Its technical core includes the AI-Assisted Annotation (AIAA) server, which allows clinicians to label complex datasets up to 10x faster using pre-trained 'Smart' models. Furthermore, Clara Train leads the market in privacy-preserving AI through its sophisticated Federated Learning (FL) implementation, enabling multi-institutional collaboration without sharing sensitive patient data. It is optimized for NVIDIA DGX and RTX systems, ensuring seamless scalability from local workstations to massive GPU clusters. In the 2026 landscape, Clara Train's capabilities are primarily accessed via NVIDIA AI Enterprise for commercial support or through the open-source MONAI framework for community-driven research, maintaining its position as the gold standard for high-fidelity medical segmentation and classification tasks.
A RESTful API service that hosts pre-trained models to provide interactive, semi-automatic segmentation tools for medical viewers.
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive clinical and operational intelligence to fight death and waste in healthcare.
Predictive medical data and clinical insights for streamlined enterprise underwriting.
The professional medical network for clinicians, providing HIPAA-compliant AI and telehealth solutions.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses a secure gRPC-based communication protocol to aggregate model weights from multiple sites without moving raw data.
Automated hyperparameter search and neural architecture search optimized for medical imaging datasets.
Built-in support for starting with high-accuracy pre-trained models on NGC.
Utilizes NVIDIA Tensor Cores for FP16/BF16 calculations while maintaining FP32 master weights.
GPU-accelerated transforms (rotation, scaling, noise) applied during training using NVIDIA DALI.
Native support for Horovod and NCCL for efficient multi-node and multi-GPU distributed training.
Urgent need to quantify lung opacity in CT scans during a pandemic surge.
Registry Updated:2/7/2026
Training a rare disease model requires data from 5 different hospitals that cannot share images.
Manual identification of cavities and implants in high-volume clinics.