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An AI tool using routine heart scans and records predicts advanced heart failure with 85% accuracy, outperforming older methods.
An AI system developed by researchers from Weill Cornell Medicine and other institutions accurately predicts advanced heart failure by analyzing routine cardiac ultrasounds and electronic health records, outperforming previous methods with 85% accuracy in identifying high-risk patients.
The tool estimates peak oxygen consumption—a key diagnostic measure—without requiring specialized cardiopulmonary exercise testing, which is often unavailable outside major medical centers.
In a separate study, another AI algorithm detected occlusive heart attacks without ST elevation in 84% of cases, significantly outperforming standard clinical methods.
Both studies, presented at the 2026 ESC Acute CardioVascular Care congress, suggest AI could improve early diagnosis and treatment access, though further validation is needed before widespread use.
Una herramienta de IA que utiliza escaneos y registros cardíacos de rutina predice insuficiencia cardíaca avanzada con un 85% de precisión, superando a los métodos más antiguos.