How do you implement offline AI for medical image analysis?

Offline medical image analysis deploys trained models in clinical settings without cloud dependency. Applications: radiology (detect nodules in chest X-rays, segment tumors in MRI/CT, classify fractures), pathology (identify malignant cells in histology slides, quantify biomarkers), ophthalmology (detect diabetic retinopathy in fundus images, glaucoma screening from OCT scans), and dermatology (classify skin lesions, track wound healing). Model architecture: use models pre-trained on medical imaging datasets (MedSAM for segmentation, CheXNet for chest X-rays, or custom-trained models on institutional data). Fine-tune on site-specific data to account for equipment variations (different scanner manufacturers produce different image characteristics). Regulatory considerations: medical AI devices require FDA 510(k) clearance or De Novo classification in the US, CE marking under EU MDR in Europe. Model modifications after approval may require re-submission. Clinical integration: deploy as PACS (Picture Archiving and Communication System) plugin or standalone workstation application, receive images via DICOM protocol, process and return results in structured format (DICOM SR for structured reports), and integrate with clinical workflows through HL7/FHIR messaging. Safety: always present results as decision support (not autonomous diagnosis), display confidence scores, implement fail-safe behavior (route to radiologist when confidence is low), and maintain audit trail of all AI assessments. Validation: test on representative patient populations, report performance metrics per demographic group, compare against expert clinician performance, and implement ongoing monitoring for performance degradation.