Latest AI and machine learning research in work force for healthcare professionals.
Using tibial sensors in total knee replacements (TKRs) can enhance patient outcomes and reduce early revision surgeries, benefitting hospitals, the National Health Services (NHS), stakeholders, biomedical companies, surgeons, and patients. Having a sensor that is accurate, precise (over the whole surface), and includes a wide range of loads is important to the success of joint force tracking. This...
Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with healthcare applications is vital to ensure equity. Two such machine learning models are NetMHCpan-4.1 and NetMHCIIpan-4.0, used to predict antigen binding scores t...
It is a common view that artificial systems could play an important role in dealing with the shortage of caregivers due to demographic change. One arg...
BACKGROUND: In the evolving landscape of microbiology and microbiome analysis, the integration of machine learning is crucial for understanding comple...
Despite the high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. Artificial Intelligence (AI...
Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers...
BACKGROUND: Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accur...
AIMS: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, thes...
BACKGROUND AND AIMS: Endoscopic submucosal dissection (ESD) for superficial esophageal cancer is a multistep treatment involving several endoscopic pr...
It is wise to investigate past and present epidemics in the hopes of profiting from them and being better prepared for future ones. COVID-19 is one of...
INTRODUCTION: Artificial intelligence (AI)-based technologies embody countless solutions in radiation oncology, yet translation of AI-assisted softwar...
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental...
Machine-learning datasets are typically characterized by measuring their size and class balance. However, there exists a richer and potentially more u...
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning (SSL) techniques, such as...
A 4-month-old previously healthy female presented with persistent nonbloody, nonbilious emesis, decreased urine output, weight loss, fussiness, and le...
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential t...
With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unkn...
Developing robust artificial intelligence (AI) models that generalize well to unseen datasets is challenging and usually requires large and variable d...
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based ap...
Generating large-scale, high-fidelity sequencing data is challenging and, furthermore, not much has been done to characterize adjuvants' effects at th...