An AI-enhanced stethoscope demonstrated superior cardiovascular disease detection capabilities when properly utilized, yet encountered significant adoption challenges in clinical practice. The pragmatic trial revealed a stark contrast between the technology's theoretical potential and its real-world performance, with low uptake rates and workflow disruptions limiting its effectiveness. This outcome exemplifies a critical pattern in digital health innovation where promising laboratory results often fail to translate into meaningful clinical impact. The cardiovascular screening space has seen numerous AI tools struggle with similar implementation hurdles, from user resistance to integration complexity. Healthcare systems typically require extensive workflow redesign and staff training to accommodate new diagnostic technologies, costs that often outweigh initial enthusiasm. The disconnect between controlled efficacy studies and pragmatic effectiveness trials continues to plague medical AI deployment, suggesting that future development should prioritize seamless integration from the earliest design phases. For clinicians and health systems, this study reinforces that technological sophistication alone cannot guarantee clinical utility—successful implementation requires equal attention to human factors, workflow compatibility, and organizational readiness.