<p>Advancements in deep learning models for medical image classification have greatly improved accuracy, yet the challenge of explainability persists, making it difficult for clinicians to trust and effectively utilize these models in decision-making. This paper presents the Shadow Learner System, a novel approach that combines Convolutional Neural Networks with Explainable AI (XAI) techniques, focusing on the value of misclassified data—both false positives and negatives—by analyzing them to uncover important insights that traditional methods might overlook. We call our framework the Duplex Computer-Aided Diagnosis System. Using the Musculoskeletal Radiographs dataset from Stanford University, which includes images of bone abnormalities that may require surgical intervention, we demonstrate how our system enhances explainability. Our findings show that about 85% of the Regions of Interest identified from misclassified images provided useful insights that clinicians found valuable in their work. Moreover, we observed that the shadow set—comprising important misclassified instances—constituted approximately 4–6% of the original dataset. This research highlights how misclassified data can reveal hidden patterns that improve the overall understanding and trust in AI models used in healthcare. The Shadow Learner System not only serves as a reference for treatment decisions but also has potential applications for other medical conditions that require surgical evaluation, such as osteonecrosis and various tumors. By shifting the focus of XAI from mere model validation to enhancing clinical interpretability, this work contributes to the more effective integration of AI in medical practice.</p>