The RED-RHD system achieved 95.62% precision for detecting abnormal heart sounds and 99% precision for classifying murmur types using acoustic analysis of heart recordings. The AI tool combines deep learning embeddings with ensemble classifiers and introduces adaptive model selection that automatically adjusts to different populations, addressing the notorious problem of poor generalization across diverse patient groups that has plagued previous cardiac AI systems. Rheumatic heart disease affects over 40 million people globally, disproportionately impacting low-resource settings where specialist cardiac screening is scarce. This technology could democratize early detection by enabling non-specialists to identify disease through simple stethoscope recordings processed by smartphone apps. The adaptive population-specific modeling represents a significant advance over static AI systems that often fail when deployed in new demographics. However, this preprint awaits peer review and the impressive metrics require validation in real-world clinical settings. The 99% precision figure, while striking, may not reflect performance complexities in actual screening programs where false positives carry significant healthcare costs and patient anxiety.