Latest AI and machine learning research in prevention of medical errors for healthcare professionals.
BACKGROUND: Patients often struggle to understand standard hospital discharge letters, increasing the risk of medication errors and misunderstandings. According to cognitive load theory (CLT), complex, information-dense texts can overload working memory and impair comprehension. Artificial intelligence tools that generate patient-centered versions could help reduce extraneous cognitive load and br...
This article investigates optimization-driven learning techniques to address the critical challenge of balancing communication efficiency with convergence acceleration in distributed multiagent systems. While existing accelerated methods typically necessitate multiple internode communications per iteration, we propose two novel methods, heavy-ball exact fusion (HBEF) and Nesterov-accelerated exact...
Fish allergy is a common food-related allergic reaction that can lead to serious health issues and poses a significant challenge to global food safety...
We aim to demonstrate the therapeutic value of the physical examination beyond its diagnostic function and to examine theoretical pathways that contri...
BACKGROUND AND AIMS: Endoscopic differentiation between autoimmune gastritis (AIG) and Helicobacter pylori-associated atrophic gastritis (HpAG) remain...
BACKGROUND: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and deme...
Artificial intelligence (AI) is increasingly integrated into everyday life. Yet in health care, patients and families are challenged to understand how...
Allosteric regulation enables proteins to couple local structural changes to distal functional outcomes, yet the underlying mechanisms often remain di...
Federated learning (FL) enables collaborative medical image analysis across decentralized institutions while preserving data privacy. However, real-wo...
BACKGROUND: Cardiovascular disease prevention relies on accurate risk assessment; however, existing scores are imprecise. Routine imaging may be oppor...
Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...
BACKGROUND: The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven he...
Large Language Models (LLMs), represented by the Generative Pretrained Transformer (GPT), are profoundly transforming the healthcare sector. Spine med...
IMPORTANCE: Depression most commonly first emerges during adolescence, making early prevention critical. While school-based mindfulness training (SBMT...
BACKGROUND: Uncertainty around a patient's prognosis at the end of life remains a major barrier to timely palliative-care involvement and alignment of...
OBJECTIVE: To investigate how AI-powered chatbots influence patient perceptions of pharmacist roles compared to traditional educational methods and ev...
The emergence of ultralarge-scale hardware systems for artificial intelligence is driving demand for high-performance heterogeneous integration. At th...
The field of prevention science seeks to identify and implement effective strategies to address social, emotional, and health challenges. A critical a...
Automatically measuring the Vertebral Heart Scale (VHS) from canine thoracic X-ray images is an effective approach for the early screening and prevent...
Device-to-device (D2D) communication is used to frequently gather and exchange information in various domains. Millimeter-wave research has also incor...