In medical AI, even a highly accurate model (e.g., 95%) carries cumulative error, meaning a small fraction of predictions can be critically incorrect. When deploying such models in real-world healthcare applications, trust in predictions is paramount. How do we ensure reliability when a model might perform unacceptably on certain cases? To address this, we developed a Confidence of Predictions (CoP) framework, inspired by how humans assess confidence based on experience. Our approach doesn’t just provide predictions, it flags cases requiring human intervention, ensuring that potential misclassifications don’t compromise patient outcomes. This proprietary innovation strengthens AI-driven healthcare workflows, making deep learning models more accurate but also reliable and accountable, a crucial step toward AI-assisted diagnostics with human-in-the-loop validation.