Latest AI and machine learning research in cultural competence for healthcare professionals.
OBJECTIVES: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data.
Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question abou...
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models ...
This perspective highlights the importance of addressing social determinants of health (SDOH) in patient health outcomes and health inequity, a global...
To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. PubMed, ...
Artificial intelligence (AI) large language models (LLMs) hold great potential to transform psychiatry and mental health care by delivering relevant a...
Diagnostic codes in the Electronic Health Record (EHR) are known to be limited in reporting patient suicidality, and especially in differentiating the...
Implicit bias can impede patient-provider interactions and lead to inequities in care. Raising awareness is key to reducing such bias, but its manifes...
Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At...
Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset ...
One potential application of neural networks (NNs) is the early-stage detection of oral cancer. This systematic review aimed to determine the level of...
As the health care landscape evolves toward value-based care and emphasizes health-related social needs, the importance of developing health policies ...
Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of...
Artificial intelligence (AI) in the form of ChatGPT has rapidly attracted attention from physicians and medical educators. While it holds great promis...
The main motivation of this paper is to introduce the ordinal diversity, a symbolic tool able to quantify the degree of diversity of multiple time ser...
Sampling a diverse set of high-quality solutions for hard optimization problems is of great practical relevance in many scientific disciplines and app...
The American Journal of Occupational Therapy (AJOT) has maintained its top-ranking status in the field of occupational therapy, as evidenced by an inc...
Professors Elham Emami and Samira Rahimi organized and co-led an international interdisciplinary workshop in June 2023 at McGill University, built upo...
Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather senso...
BACKGROUND: Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's dec...