Latest AI and machine learning research in staffing & scheduling for healthcare professionals.
The joint use of multiple modalities for medical image processing has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for a lot of medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used multi-modality medical images due to its ease of acquisition and the effectiveness for...
In the 21st century, health care has been going through a paradigm shift called digital health. Due to major advances and breakthroughs in information technologies, most recently artificial intelligence, the patriarchy of the doctor-patient relationship has started evolving toward an equal-level partnership with initial signs of patient autonomy. Being an underused resource for centuries, patients...
Healthcare activities in hospitals generate numerous types of post-use waste materials that can be classified as hazardous. This study proposes an Art...
Over the past 40 years, diagnostics have become the backbone of HIV prevention, treatment, and retention in care, and are central to the achievement o...
Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovas...
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analy...
The healthcare sector is undergoing a profound transformation driven by the rapid rise in healthcare applications (mHealth apps), which are becoming i...
The rapid growth of the healthcare industry in China has led to a significant talent gap, particularly in the areas of digital skills and management e...
Digital pathology (DP) has significantly transformed breast pathology at Mount Sinai Hospital by enhancing diagnostic accuracy, collaboration, and edu...
Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome characterized by variable pathophysiology and different therapeutic ap...
Turnaround time (TAT) has evolved into a complex metric in the current era of diagnostic radiology. Initially monitoring a radiologist's ability to ef...
PURPOSE: This review aims to provide a comprehensive overview of the transformative impact of FDA-approved artificial intelligence (AI) products in ab...
The performance of electrochemical sensors is influenced by various factors. To enhance the effectiveness of these sensors, it is crucial to find the ...
OBJECTIVE: Racial discrimination drives health disparities among racial/ethnic minority youth, creating chronic stress that affects brain development ...
Maintaining the balance and safety of the exoskeleton human-robot coupling system is a prerequisite for realizing the rehabilitation training function...
This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics,...
Despite artificial intelligence's (AI) transformative potential in healthcare, nursing workforce scholarship lacks a cohesive theoretical foundation ...
Non-target screening (NTS) using chromatography coupled to high-resolution mass spectrometry (HRMS), has become fundamental for detecting and prioriti...
There is increasing interest in using assistive robotic devices to support motor re-learning and recovery in individuals with neurological impairments...
BackgroundIn this research, we explore the application of Convolutional Neural Networks (CNNs) for the development of an automated cancer detection sy...