AI-analyzed echocardiograms identified patients with transthyretin cardiomyopathy facing 6-fold higher risk of death or heart failure hospitalization compared to low-risk patients. The study of 347 patients revealed that automated measurements of left ventricular global longitudinal strain and right ventricular fractional area change independently predicted outcomes, with hazard ratios of 1.13 and 0.96 respectively. This artificial intelligence approach enhanced risk stratification beyond traditional biomarker staging systems, increasing predictive power from 53 to 80 on statistical measures. The finding represents a significant advance for managing this progressive, fatal disease affecting primarily elderly men. Transthyretin cardiomyopathy, caused by protein deposits in heart muscle, has historically been difficult to risk-stratify accurately. The AI tool's performance matched human expert analysis while offering automated, potentially more consistent assessment. This could democratize sophisticated cardiac evaluation across healthcare systems with varying expertise levels. However, as a preprint awaiting peer review, these results require validation through the formal scientific review process. The retrospective design and relatively short 2.4-year follow-up period also limit immediate clinical application, though the technology shows promise for improving personalized treatment decisions in this challenging condition.