A prospective, two-phase observer-performance study reported that a confidence-aware AI system improved radiologists’ accuracy and agreement in distinguishing two common benign parotid tumors on ultrasound. The system included a radiologist-editable interface and was evaluated in a structured real-world setting. The design addresses a recurrent barrier in clinical AI deployment: models often demonstrate technical accuracy without demonstrating that human readers accept the output or that diagnostic disagreement decreases in practice. By measuring observer performance, the study focuses on the human-AI loop. For the imaging and diagnostic device market, the result supports near-term applications where AI can act as a decision support layer, particularly in settings constrained by experience levels. Still, the field will continue to demand external validation and evidence that the tool affects treatment choices and patient outcomes, not only reader metrics.