Discover the newest research about AI innovations in π©Ή Emergency.
Machine learning predicts 30-day mortality in diabetes patients at ED with 86-97% sensitivity. πβοΈ
Deep learning model detects orbital fractures in youth radiographs: AUROC 0.802, sensitivity 65.8%, specificity 86.5%. πποΈ
Remote MRI scanning pilot reduces patient wait times and DNA rates, enhancing flexibility and training efficiency. π₯π
Lung lobe segmentation tools evaluated: TotalSegmentator excels, while data diversity enhances model accuracy. ππ«
AI in Emergency Toxicology: Enhancing Decision-Making Amidst Challenges π€β οΈ
AI aids emergency rooms in predicting patient admissions earlier, potentially improving care and reducing overcrowding. π₯π€
Exploring AI’s impact on emergency nursing support during explosive attacks: key findings from Seyedin et al. ππ€
New ambulance software in Rwanda aims to improve emergency care π. It could save nearly one million lives through faster coordination. π
Bimodal imaging probes enhance disease diagnosis and therapy, combining MRI and fluorescence for improved precision. π§¬π¬
Open-source AI tools can assist doctors in reading medical scans effectively and affordably, ensuring patient privacy. π₯π€
