Section
Highlight Deep learning applied to 12-lead ECG signals enables estimation of 10-year ischemic stroke risk with moderate discrimination and good calibr
Highlight This unprecedented large-scale multinational study developed a semisupervised contrastive machine learning framework that generates a global
A pilot study demonstrates that ChatGPT-4 can feasibly collect accurate and satisfactory medical histories from pediatric emergency patients, streamlining triage and reducing documentation burden.
This article explores AMBUSH-AI, a novel AI-driven system utilizing ultrasound modalities to accurately predict burn wound depth, improving diagnostic accuracy beyond current clinical standards and potentially guiding optimal surgical decis
A prospective multicenter study demonstrates that AI-driven virtual native enhancement (VNE) MRI can accurately detect and quantify myocardial scars without contrast agents, potentially reducing the need for gadolinium-based imaging in clin
Recent research demonstrates that machine learning algorithms can accurately predict patient-reported outcomes one year after breast reconstruction, potentially enhancing shared decision-making and individualized care planning.
A pilot study evaluating Ray-Ban Meta AI eyeglasses demonstrated high accuracy in single object identification and moderate performance in descriptive tasks, suggesting promise for aiding individuals with low vision.
A 2026 expert consensus defines an image-based reference standard to validate AI screening algorithms for age-related macular degeneration (AMD), endorsing Beckman grading and routine OCT use while clarifying referral thresholds and areas o
The MAIPI deep learning model offers a novel, accurate prognosis tool for mantle cell lymphoma, using standard H&E stained biopsy images, bypassing need for molecular tests or expert pathology, and enhancing risk stratification for indi
A novel artificial intelligence algorithm enables continuous, noninvasive monitoring of inspiratory muscle effort and patient-ventilator dyssynchrony during mechanical ventilation, demonstrating accuracy comparable to invasive gold standard
A study comparing physician-based surprise question, AI-driven GEST model, and a combined approach shows improved accuracy and efficiency in predicting 6-month mortality among older ED patients.
A machine learning algorithm can generate synthetic CT images from temporal bone MRI, enabling visualization of bone and soft tissue simultaneously without radiation, useful for otologic surgical planning and localization.
In a 14-institution cohort of more than 508,000 operations, MySurgeryRisk maintained high discrimination for ICU admission, mechanical ventilation, acute kidney injury, and in-hospital mortality using routine electronic health record data.
A fine-tuned ensemble of tabular foundation models moderately predicted pathological complete response after TNT and showed potential to support watch-and-wait selection after recalibration.
In a large Veterans Health Administration cluster randomized study, CADe availability increased adenoma detection during colonoscopy without changing withdrawal time or nonadenomatous lesion detection.
Unsupervised machine learning identified three distinct preeclampsia phenotypes linked to timing of delivery, placental dysfunction, fetal growth, and complications, suggesting a path toward more personalized risk assessment and management.
A major 2026 study suggests donor age affects survival differently in haploidentical and matched unrelated stem cell transplants, offering a more practical framework for choosing the best donor.
A qualitative study found that ChatGPT 3.5 described “great surgeons” differently by demographic identity, echoing stereotypes that may shape evaluations, leadership perceptions, and inclusion in surgery.
In Kenyan outpatient clinics, an AI-enabled ECG algorithm showed strong discrimination for left ventricular systolic dysfunction, with 95.6% sensitivity and 99.1% negative predictive value against echocardiography.
In a real-world academic emergency department, ambient AI scribe use was uncommon and concentrated among a small group of physicians, but when used it was associated with shorter documentation and total EHR time.
