Disclaimer: This article was created for the purpose of entering the All Things Agentic Hackathon hosted by Google Cloud and Devpost.

Radiologists worldwide face extreme burnout, evaluating 50+ volumetric MRI scans daily. Each scan contains 24 to 40 high-resolution DICOM slices. Manually inspecting every plane, checking ligament continuity across depth slices, and dictating repetitive clinical reports takes 10 to 15 minutes per study.

We built RadScan AI to eliminate this workflow bottleneck.

By combining GCP Cloud Run (L4 GPU scale-to-zero microservices), a 2.5D Volumetric CNN-BiGRU Neural Network (trained on 819,100 DICOMs / 530 GB data), and Vertex AI Gemini 3.5 Flash / 1.5 Pro, RadScan AI acts as an autonomous radiology co-pilot that performs 12-target pathology detection, pinpoints lesion coordinates with Grad-CAM visual heatmaps, and drafts structured DICOM reports in under 3 seconds—saving radiologists ~6 minutes per scan.

RadScan AI is architected as two decoupled, serverless microservices on Google Cloud Platform: