Models built on images and clinical variables show off their technical prowess, especially in text and imaging, sometimes skipping past the grind of feature engineering. But most studies lack external validation and pay little attention to the seniors who make up much of the real-world patient base. The gap between synthetic success and hospital-side impact remains wide, especially for older-adult care teams who need both discrimination and calibration.
Deep Learning for Cardiac Procedures: Accurate, Automated, but Unchecked?
Are deep learning models for prediction after major cardiac procedures ready for the uncertainties of actual practice?