Latest AI and machine learning research in risk management for healthcare professionals.
BACKGROUND: There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models remain uncertain. OBJECTIVE: This systematic review aims to evaluate the predictive performance and methodological quality of machine learning models for venous thromboembolism risk after joint replacement surgery. ME...
BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals living with cancer, who often assume central roles in care coordination, treatment adherence, symptom monitoring, and emotional support. Rates of distress among caregivers frequently equal or exceed those observed in patients themselves. Despite incr...
PURPOSE OF REVIEW: To synthesize recent advances in intraoperative resuscitation for trauma surgery, including fluid composition, transfusion threshol...
The use of machine learning (ML) models in forensic anthropology (FA) has increased in the last half decade; however, there is a lack of a standardize...
BACKGROUND: Self-reported, computerized history taking (CHT) may enable efficient collection of medical histories for acute chest pain management. OBJ...
Seismocardiography (SCG), a non-invasive method for capturing cardio-mechanical signals, is often susceptible to noise and motion artifacts. Current a...
Accurate characterization of shaley-sand reservoirs remains a significant challenge in petroleum geophysics, where complex clay mineralogy often rende...
The rapid growth of the Internet of Medical Things (IoMT) has increased the adoption of remote healthcare applications and telemedicine services. A Ma...
This study aims to synthesize the perceptions and expectations of long-term caregivers regarding the use of nursing robots to inform strategies for en...
PURPOSE: The purpose of this study was to explore whether incorporating gyroscopic and accelerometer data will improve the prediction of energy expend...
OBJECTIVES: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm. MATE...
OBJECTIVES: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. ...
The generation of realistic network traffic is a critical requirement for testing, simulation, and security evaluation in ZigBee-based IoT systems. In...
RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...
PURPOSE OF REVIEW: Post-traumatic care is evolving from a reactive, protocol-driven paradigm to a predictive, personalized approach. This review exami...
A COMMENTARY ON: Ziaei, S., Samani, D., Behjati, M. et al. Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systema...
BACKGROUND: Early identification of disability risk in community-dwelling older adults has emerged as a critical public health priority. An increasing...
Obstetric anaesthetists work in high-acuity settings where maternal and fetal conditions can deteriorate rapidly, requiring coordinated action across ...
We describe a method for determining the ultrastructural organization of axons and varicosities of cultured dorsal root ganglion (DRG) neurons using c...
As use of artificial intelligence algorithms in clinical practice becomes more commonplace, radiologists may become overconfident in the abilities of ...