GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis

Prototype-based medical image classifiers have three clinical gaps: they treat findings as independent, silently amplify unsafe doctor feedback, and require full retraining whenever a new finding is needed. We present GRAPE (Graph-Augmented Prototype Explanations), a unified architecture that closes all three gaps. A Graph Attention Task Head models anatomical concept co-occurrence, boosting macro-F1 by +13.8 pp over the prototype baseline on TBX11K. A Concept-Mismatch Safety Check is the first such mechanism in prototype-based medical classifiers, warns when the model's dominant finding inside a doctor-drawn region conflicts with the claimed label, catching 85% of erroneous annotations versus 51% for MC-Dropout with no extra inference cost. Open-Vocabulary Prototype Anchoring aligns visual prototypes to clinical text, so a new finding can be added from a single labelled image without modifying any other component: on NIH ChestX-ray14, one Effusion example recovers full-supervision localisation accuracy; on TBX11K, prototype maps achieve $2.6\times$ better lesion localisation than end-to-end baselines. All three capabilities add only $+1$ ms latency at interactive batch size.