EchoAtlas, trained on 12.9 million question-answer pairs from 2 million echocardiogram videos, achieved 96.6% accuracy on internal cardiac imaging interpretation tests and significantly outperformed existing models on the MIMIC-EchoQA benchmark (69.9% vs 50.8%). The autoregressive vision-language model can perform quantitative measurements, assess regional wall motion, and provide diagnostic reasoning across multiple question formats. This represents a substantial leap in AI-assisted cardiac imaging interpretation, potentially addressing the critical bottleneck in echocardiography analysis where demand far exceeds specialist availability. Current cardiac imaging relies heavily on subjective human interpretation, creating variability and access limitations. EchoAtlas could democratize expert-level cardiac assessment, particularly valuable in underserved areas lacking specialized cardiologists. However, the model requires validation across diverse patient populations and healthcare settings before clinical deployment. As a preprint awaiting peer review, these impressive results need independent confirmation and rigorous testing for potential biases or failure modes. The integration of visual analysis with clinical reasoning marks a paradigm shift from narrow AI tools to comprehensive diagnostic assistance, though regulatory approval and real-world performance validation remain crucial hurdles before transforming cardiac care delivery.