Latest AI and machine learning research in cultural competence for healthcare professionals.
The entrustment framework redirects assessment from considering only trainees' competence to decision-making about their readiness to perform clinical tasks independently. Since trainees and supervisors both contribute to entrustment decisions, we examined the cognitive and affective factors that underly their negotiation of trust, and whether trainee demographic characteristics may bias them. Usi...
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast ...
Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the time...
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role ...
. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the preci...
Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven ...
As population density increases, environmental hygiene and public health become increasingly severe. As the space where residents stay for the longest...
Convolutional Neural Networks have been widely applied in medical image segmentation. However, the existence of local inductive bias in convolutional ...
There has been a growing recognition of the need for diversity and inclusion in scientific fields. This trend is reflected in the Journal of Chemical ...
BACKGROUND: ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonethele...
Artificial intelligence (AI)-assisted diagnosis is an ongoing revolution in pathology. However, a frequent drawback of AI models is their propension t...
Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where th...
Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, i...
Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP)...
Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers...
AIMS: Medical case vignettes play a crucial role in medical education, yet they often fail to authentically represent diverse patients. Moreover, thes...
Artificial intelligence (AI) in medicine and dermatology brings additional challenges related to bias, transparency, ethics, security, and inequality....
The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency ...
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...