Explaining artificial intelligence with visual analytics in healthcare

Abstract

To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well-considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms’ outputs. One approach is visual analytics, which integrates humans in decision-making through visualizations that facilitate interaction with algorithms. Although many visual analytics systems have been developed for healthcare, a clear overview of their explanation techniques is lacking. Therefore, we review 71 visual analytics systems for healthcare, and analyze how they explain advanced algorithms through visualization, interaction, shepherding, and direct explanation. Based on our analysis, we outline research opportunities and challenges to further guide the exciting rapprochement of visual analytics and healthcare.