Radiology AI

An advanced AI-assisted radiology workflow system designed to streamline and enhance the medical imaging interpretation process by combining state-of-the-art medical image segmentation with vision-language report generation capabilities. The platform intelligently analyzes radiological scans such as CT, MRI, X-ray, and ultrasound images, automatically identifying and segmenting clinically relevant anatomical structures, lesions, abnormalities, and regions of interest with high precision. Leveraging fine-tuned deep learning models specialized for medical imaging tasks, the system generates structured draft radiology reports that summarize findings, impressions, and potential diagnostic indicators in natural clinical language, significantly reducing reporting turnaround time while supporting physician productivity. The solution includes support for ROCm (Radeon Open Compute), enabling efficient deployment and acceleration on AMD GPU infrastructure. ROCm compatibility provides organizations with greater hardware flexibility, cost efficiency, and scalable performance for training and inference workloads in medical AI environments. This makes the system suitable for institutions seeking high-performance AI capabilities while leveraging AMD-based compute ecosystems in both on-premise and cloud deployments. Additional capabilities may include DICOM integration, PACS interoperability, configurable reporting templates, multimodal clinical context incorporation, automated prioritization workflows, segmentation visualization overlays, and support for human-in-the-loop validation pipelines. By reducing repetitive reporting tasks and accelerating preliminary analysis, the platform helps radiologists focus more on diagnostic decision-making, quality assurance, and patient care while maintaining clinical oversight and regulatory compliance.