In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

Background Previous studies in medical imaging have shown disparate abilities of artificial
intelligence (AI) to detect a person's race, yet there is no known correlation for race on …

The black-box nature of current artificial intelligence (AI) has caused some to question
whether AI must be explainable to be used in high-stakes scenarios such as medicine. It has …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Recent years have seen a rapid increase in the use of behavioural science to address the
priorities of public and private sector actors. There is now a vibrant ecosystem of …

Health equity is a primary goal of healthcare stakeholders: patients and their advocacy
groups, clinicians, other providers and their professional societies, bioethicists, payors and …

Minimizing bias is critical to adoption and implementation of machine learning (ML) in
clinical practice. Systematic mathematical biases produce consistent and reproducible …

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is
recognized, but it can also exacerbate these issues if not implemented in an equitable …

Abstract Machine learning-based clinical decision support tools for sepsis create
opportunities to identify at-risk patients and initiate treatments at early time points, which is …