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 …

In this intellectual work, the clinical and educational aspects of dentistry were confronted
with practical applications of artificial intelligence (AI). The aim was to provide an up-to-date …

The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

As artificial intelligence (AI) rapidly approaches human-level performance in medical
imaging, it is crucial that it does not exacerbate or propagate healthcare disparities. Previous …

Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …

Abstract Health Professions Education (HPE) has benefitted from the advances in Artificial
Intelligence (AI) and is set to benefit more in the future. Just as any technological advance …

AbstractScale the model, scale the data, scale the compute'is the reigning sentiment in the
world of generative AI today. While the impact of model scaling has been extensively …

A plethora of work has shown that AI systems can systematically and unfairly be biased
against certain populations in multiple scenarios. The field of medical imaging, where AI …

Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

As machine learning methods gain prominence within clinical decision-making, the need to
address fairness concerns becomes increasingly urgent. Despite considerable work …