Researchers developed an interpretable machine learning model and web-based tool to forecast postoperative in-hospital mortality in patients with acute aortic ...
A European survey of 874 patient community respondents found widespread recognition of healthcare artificial intelligence benefits alongside significant concerns regarding ...
A systematic review and pooled meta-analysis of four clinical studies found that large language models achieved 73 percent concordance with ...
A blinded study in BMC Urology found no significant difference in quality between three LLM systems and two urologists for ...
A review in Cell Division evaluates AI and machine learning models for pancreatic cancer diagnosis, highlighting their high reliability in ...
A mini-review proposes an EHR-integrated framework combining quantitative ocular biometrics and automated risk stratification to improve perioperative decision-making in ophthalmology.
Researchers propose a causal governance framework and multi-agent architecture to distinguish clinically justified contextual differences from harmful algorithmic bias in ...
A scientific review published in BMC Artificial Intelligence evaluates how artificial intelligence, robotics, and natural language processing advance telemedicine and ...
A systematic review of 35 studies demonstrates that machine learning models analyzing structured clinical, imaging, and demographic data achieve diagnostic ...
A systematic review and meta-analysis reveals that artificial intelligence decision support significantly enhances fracture detection sensitivity for clinicians, delivering the ...
A new machine learning model developed by researchers predicts the risk of recurrence in patients with borderline ovarian tumors undergoing ...
An exploratory vignette study found that large language models matched physicians in tuberculosis diagnostic accuracy while scoring higher on structured ...
A multicentre derivation and validation study paired with a prospective randomised trial shows an explainable artificial intelligence ensemble predicts multiple ...
A new artificial intelligence framework named Explainable Apprenticeship Learning formulates clinical diagnostic reasoning as a hierarchical policy learning problem derived ...
A random forest machine learning model achieved strong predictive performance for acute respiratory distress syndrome among critically ill patients with ...
A support vector machine model predicted gastrointestinal bleeding within 72 hours after endovascular thrombectomy in a two-center retrospective study of ...
An explainable XGBoost model using eight routine clinical parameters predicted sepsis-associated acute kidney injury in 3,437 children across five Chinese ...
A narrative review indicates that procalcitonin and C-reactive protein optimize antibiotic utilization in hospital-acquired pneumonia when interpreted through serial kinetic ...
A new physiology-informed neural network with attributable branches predicts blood glucose in type 1 diabetes while revealing which factors drive ...
Oxford researchers built an AI method that estimates uncertainty in landmark-based hip measurements and converts it into a screening confidence ...
