RadAgent: Grounded Radiology AI

RadAgent is a grounded multimodal radiology AI system designed to improve the reliability and transparency of AI-assisted clinical reporting. While modern Vision-Language Models can generate radiology reports directly from X-rays, they may also hallucinate findings or produce unsupported conclusions. In healthcare, where patient safety depends on accurate clinical decisions, explainability and trust are essential. RadAgent addresses this challenge through a multi-agent pipeline built for evidence-grounded reasoning. A specialist chest X-ray model first analyzes the image and predicts multiple thoracic pathologies using calibrated class-specific thresholds. Instead of passing raw predictions directly to a language model, validated findings are transformed into structured medical queries and enriched through a Retrieval-Augmented Generation (RAG) pipeline that retrieves trusted clinical knowledge. To improve interpretability, Grad-CAM visualizations highlight the image regions that influenced each prediction. A multimodal vision-language model then combines image features, calibrated findings, and retrieved evidence to generate a structured radiology report grounded in both image-derived information and external medical references. Reports can be exported as professional PDFs and translated into multiple languages while preserving clinical meaning. RadAgent is optimized for AMD Instinct™ MI300X using ROCm and vLLM, enabling efficient deployment of multiple AI components within a unified inference pipeline. More than a report generator, RadAgent demonstrates a practical approach to trustworthy medical AI—where every prediction is explainable, every report is evidence-backed, and every clinical conclusion is designed to earn clinicians' confidence.