Hospitals in many developing regions face long delays in radiology reporting due to a shortage of radiologists and heavy workloads. Although AI has achieved strong results in medical imaging research, most systems still fail when used in real clinical settings because they lack transparency, adaptability, and interpretability. The project deals with the mismatch between the best performing AI models in research and their safe use in clinical practice. The main aim is to create a system that gives accurate and explainable results so that the radiologists would be able to depend on the AI support in their everyday diagnostic work.

The foremost hurdle is to make AI systems clinically usable, as they are now black boxes, giving out results without any explanation or reasoning. Present-day models are usually designed for a particular disease or imaging modality and lack the necessary flexibility for widespread clinical use. The project intends to rectify these issues by creating a framework that would not only predict diseases but also give reasons for its predictions and switch between different imaging types. Moreover, it will target ease of access to the system through a web interface, cloud platform deployment, and by making it user-friendly for doctors in their normal diagnostic process.

We present a three-tier agentic architecture that accurately imitates hospital workflows for medical imaging interpretation and reporting. The architecture consists of three layers, namely Generalist, Specialist, and Super-Specialist. The Generalist is the reasoning agent that recognizes the case type, identifies the relevant body region, and then passes the task to the appropriate Specialist models. The Specialists are responsible for the disease-specific tasks, whereas the Super-Specialists are in charge of the detailed analyses, such as segmentation or severity grading.

The architecture is built in such a way that it is flexible and trustworthy. The models execute the tasks separately; hence, each model focuses on its own strengths while keeping the clinical context. Moreover, the system can also accommodate new diseases by instantaneously merging the available Specialist modules. Every single output is traceable throughout the entire process, from reasoning to final prediction, which means that the method is both explainable and safe in terms of research and clinical applications.