How do you build offline AI pipelines for medical image analysis?
Offline medical image analysis pipelines run on local workstations or servers within healthcare facilities. The pipeline typically includes DICOM image ingestion from the PACS system, preprocessing for normalization and augmentation, inference using specialized models for tasks like tumor detection, organ segmentation, or pathology classification, and result integration back into the clinical workflow. Models like Med-SAM for segmentation, CheXNet for chest X-rays, and RetFound for retinal imaging can run locally using PyTorch or ONNX Runtime. MONAI (Medical Open Network for AI) provides a comprehensive framework for medical imaging AI with privacy-preserving training capabilities. All processing occurs within the hospital network, maintaining HIPAA compliance and ensuring patient imaging data never leaves the facility.