Latest AI and machine learning research in risk management for healthcare professionals.
The development of an innovative functional assessment procedure based on the combination of electroencephalography (EEG) and robot-assisted upper limb devices may provide new insights into the dynamics of cortical reorganization promoted by rehabilitation. The aim of this study was to evaluate changes in event-related synchronization/desynchronization (ERS/ERD) in alpha and beta bands in a patien...
According to the crowd within effect, the average of two estimates from one person tends to be more accurate than a single estimate of that person. The effect implies that the well documented wisdom of the crowd effect-the crowd's average estimate tends to be more accurate than the individual estimates-can be obtained within a single individual. In this paper, we performed a high-powered, pre-regi...
Under current systemic treatment of metastatic cancer, a drug is frequently prescribed at maximum tolerable dose (MTD) until either unacceptable toxic...
Remote voice studies often retain a final audio file with limited evidence about how it was captured, transferred, processed, and accepted. This paper...
Answering what-if queries about a scene with a VLM usually means injecting the assumption as text or repainting the scene with a generative model. We ...
Industrial inspection pipelines often restore a measured image before a detector acts on it, yet restoration can suppress detector-supported defect st...
Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded by it. The literature treats these as tw...
Verifying that manufactured batches of milling tools or carbide rotary burrs conform to production order sheets remains a largely manual and error-pro...
Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under real-world acquisition variability, artifac...
Counterfactual audits are the standard tool for checking whether a clinical agent treats demographically distinct but clinically identical patients di...
Background: Consumer use of AI chatbots for health advice is rising, yet triage safety relative to established services remains unclear. Australia's H...
General-purpose vision-language models (VLMs) now support strong visual recognition, instruction following, and generation. However, most pretrained v...
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce vis...
Hyperspectral image classification still relies heavily on random pixel splits within a single scene. The Salinas dataset, randomly split, is among th...
Low-quality face recognition (LQFR) remains challenging due to the difficulty of matching degraded query (probe) images against low-quality (LQ) enrol...
Background: Systematic reviews of clinical prediction models increasingly include studies using artificial intelligence (AI) and machine learning (ML)...
Background. Cardiovascular-outcomes trials are lengthy, costly, and associated with substantial uncertainty prior to readout. In-silico trial simulati...
Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, ...
Background and Objective: Quality control is a prerequisite for whole-slide image analysis, yet the benchmarks on which quality-control methods are co...
Artistic Text Recognition (ATR) remains challenging because word images often combine decorative fonts, curved layouts, object-like characters, clutte...