Seeking an experienced full-stack/ML developer to build a secure, cloud-based web platform for analyzing anonymized dental X-rays. The tool enables upload of DICOM files, automatic anonymization (strip all metadata/identifiers), AI-driven detection & annotation of pathologies (caries, periodontal disease, bone loss, cysts, etc.), visual overlays, confidence scores, and report generation—all with strict patient privacy, no storage of originals, and human oversight required. Key Requirements: • Clean React/Next.js frontend with drag-and-drop upload, DICOM viewer (e.g., cornerstone.js), annotation overlays & heatmaps. • Python backend (FastAPI preferred) + secure auth, encrypted file handling, and cloud storage (AWS S3/GCP). • PyTorch/TensorFlow ML models (fine-tune YOLO/U-Net/MONAI on open dental datasets) for multi-label detection/segmentation. • Mandatory: Full anonymization on upload (pydicom/deid), end-to-end encryption, audit logs, compliance-ready (HIPAA/GDPR/APP principles), ethical transparency (e.g., explainability features). • Cloud deployment (AWS/GCP/Azure, serverless ideal). NDA required. Bid with experience in medical AI, proposed stack, and quote. Looking forward to ethical, privacy-first collaboration!