Discover the newest research about AI innovations in π Critical Care.
Most clinical AI models fail when they leave their home hospital, but a new 26-model suite proves that reproducible clinical tools can survive the transition to different healthcare systems.
A widespread programming oversight in clinical algorithms means AI could recommend the wrong sepsis treatment nearly half the time.
A new reanalysis reveals that highly praised machine learning survival models underperform both human doctors and a thirty-year-old statistical formula at predicting patient death at critical clinical milestones.
A massive clinical trial is putting machine learning in charge of life-or-death oxygen decisions for twenty-four thousand critically ill patients.
By predicting brain pressure from routine heart and blood signals, a new deep learning model challenges the necessity of invasive skull-drilling in intensive care units.
A new machine learning pipeline proves that algorithms can label millions of breathing mismatches without losing accuracy, bypassing the human expert bottleneck in intensive care.
Off-the-shelf automated machine learning can flag deadly hospital-acquired infections, but only if hospitals feed them the right clinical data.
A new study reveals that basic physiological math outpaces complex large language models at predicting patient crash times.
“Exploring the Actionable Innovation Day model: 28 recommendations for critical care improvement! π₯π‘”
Machine learning predicts positive blood cultures in ICU patients using vital signs. Accuracy: AUC 0.700 internal, 0.679 external. ππ©Έ
