DiagnosticAI: Medical Imaging Analysis Suite

We developed DiagnosticAI using a federated learning architecture that trains shared models across all 12 hospital sites without centralizing patient data. Each hospital runs a local training node that computes model updates on its own data, sending only encrypted gradient updates to a central aggregation server. This approach enabled us to train on a combined dataset of over 8 million imaging studies while maintaining strict data sovereignty at each institution.

The radiology module includes specialized models for chest X-ray triage, CT pulmonary embolism detection, brain MRI abnormality classification, and mammography screening. Each model provides heat map overlays highlighting regions of concern, quantitative measurements, and structured reports that integrate directly into the radiologist's workflow. A priority reordering system surfaces studies with suspected critical findings to the top of the worklist, reducing time-to-report for urgent cases by 73%.

For pathology, we built a whole-slide image analysis pipeline that performs automated cell counting, mitotic figure detection, tumor grading, and margin assessment for the most common surgical pathology specimens. The dermatology module analyzes clinical photographs and dermoscopic images, classifying lesions across 47 diagnostic categories with sensitivity exceeding 92% for melanoma detection.

The entire platform was designed for the FDA 510(k) regulatory pathway, with comprehensive validation studies, performance benchmarking against board-certified specialists, and a quality management system that tracks model performance in real time. We implemented automated drift detection to identify when model accuracy degrades for specific imaging modalities or patient populations, triggering retraining cycles before clinical impact occurs.