Latest AI and machine learning research in work force for healthcare professionals.
High performance of deep learning models on medical image segmentation greatly relies on large amount of pixel-wise annotated data, yet annotations are costly to collect. How to obtain high accuracy segmentation labels of medical images with limited cost (e.g. time) becomes an urgent problem. Active learning can reduce the annotation cost of image segmentation, but it faces three challenges: the c...
Artificial intelligence-based models and robust computational methods have expedited the data-to-knowledge trajectory in precision medicine. Although machine learning models have been widely applied in medical data analysis, some barriers are yet to be challenging, such as available biosample shortage, prohibitive costs, rare diseases, and ethical considerations. Transcriptomics, an omics approach...
Having to face the challenges posed by a shortage of skilled care workers and an increasing number of older people in need of care, policy makers and ...
This is an era of uncertainty, during which adaptability is a key capability to survival and future success. What has Singapore done to develop an edu...
Diversity is supposed to create better groups and societies but sometimes fails. It is explained why the power of diversity may not create better grou...
BACKGROUND: Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such...
During the early six months after the onset of a stroke, patients usually remain disabled with limbs weakness and need intensive rehabilitation. An in...
. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed fo...
Semi-supervised learning (SSL) methods show their powerful performance to deal with the issue of data shortage in the field of medical image segmentat...
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL a...
BACKGROUND: Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artifi...
OBJECTIVES: Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and...
The shortage of available water resources and climate change are major factors affecting agricultural irrigation. In order to improve the irrigation w...
Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehab...
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or...
BACKGROUND: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professiona...
This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the tec...
CLINICAL/METHODICAL ISSUE: Radiological procedures play a crucial role in the diagnosis of small bowel disease. Due to a broad and quite nonspecific s...
While the Metaverse is becoming a popular trend and drawing much attention from academia, society, and businesses, processing cores used in its infras...
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chi...