Imagine an AI that not only diagnoses medical images but also explains its reasoning, fostering trust and transparency in healthcare. Researchers are tackling the “black box” nature of deep learning in medical image analysis, particularly for chest X-rays (CXRs). While AI excels at classifying images, understanding *why* it arrives at a specific diagnosis has been a major roadblock to wider clinical adoption. This new research introduces a clever combination of concept bottleneck models (CBMs) and a multi-agent retrieval-augmented generation (RAG) system. Think of it like this: CBMs act as translators, linking visual features in the X-ray to human-understandable medical concepts. Then, a team of specialized AI agents steps in, similar to a doctor consulting medical literature and combining that knowledge with the CBM’s insights. The result? A detailed, interpretable radiology report that sheds light on the AI's decision-making process. Tested on a large dataset of CXRs, this innovative approach achieved high accuracy while also providing robust, understandable reports. The use of multiple AI agents, each with its own specialty, enhances the quality and clinical relevance of the generated reports. This marks a significant stride towards trustworthy AI-driven medical image analysis, giving radiologists the confidence to use these powerful tools in their practice. Challenges remain, however, particularly in adapting this approach to other medical imaging modalities like MRIs and CT scans. The future of this research lies in refining these multi-agent systems for even greater adaptability and robustness, potentially revolutionizing how we diagnose and understand medical images.