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 …

Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …

Recent progress on few-shot learning largely relies on annotated data for meta-learning:
base classes sampled from the same domain as the novel classes. However, in many …

How does the accuracy of deep neural network models trained to classify clinical images of
skin conditions vary across skin color? While recent studies demonstrate computer vision …

Importance The use of artificial intelligence (AI) is accelerating in all aspects of medicine and
has the potential to transform clinical care and dermatology workflows. However, to develop …

To investigate the well-observed racial disparities in computer vision systems that analyze
images of humans, researchers have turned to skin tone as a more objective annotation …

While artificial intelligence (AI) holds promise for supporting healthcare providers and
improving the accuracy of medical diagnoses, a lack of transparency in the composition of …

In the current development and deployment of many artificial intelligence (AI) systems in
healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent …

The recent adoption of machine learning models in high-risk settings such as medicine has
increased demand for developments in privacy and fairness. Rebalancing skewed datasets …

Facial recognition is one of the most academically studied and industrially developed areas
within computer vision where we readily find associated applications deployed globally. This …