Discover the newest research about AI innovations in π Critical Care.
A new multi-agent AI pipeline proves that the safest way to use language models in hospitals is to stop treating them as autonomous writers and start using them as structured conflict detectors.
A new machine learning model predicts 30-day mortality for brain bleed patients using routine clinical data instead of expensive brain scans, challenging the assumption that advanced imaging is required for accurate prognosis.
A new causal AI model shows that straying from its vasopressor dosing recommendations is tied to a fivefold increase in hospital mortality for septic shock patients.
An ambitious clinical trial across Australasia is about to test whether machine learning can make split-second decisions to save critically ill patients.
Automated medical registries promise to slash administrative burdens, but a new trial reveals that large language models are not yet reliable enough to replace human chart reviewers.
A new machine learning model can help hospitals predict which heart surgery patients will get stuck in the ICU, but its performance drop in external testing highlights a persistent hurdle for clinical AI.
A patientβs survival in the ICU may depend on how quickly clinicians can spot silent, ongoing seizures in the brain.
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.
