Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems

AbstractOngoing research efforts have been examining how to utilize artificial intelligence technology to help healthcare consumers make sense of their clinical data, such as diagnostic radiology reports. How to promote the acceptance of such novel technology is a heated research topic. Recent studies highlight the importance of providing local explanations about AI prediction and model performance to help users determine whether to trust AI ’s predictions. Despite some efforts, limited empirical research has been conducted to quantitatively measure how AI explanations impact healthcare consumers’ perceptions of using patient-facing, AI-powered healthcare systems. The aim of this study is to evaluate the effects of different AI exp lanations on people's perceptions of AI-powered healthcare system. In this work, we designed and deployed a large-scale experiment (N = 3,423) on Amazon Mechanical Turk (MTurk) to evaluate the effects of AI explanations on people's perceptions in the context of comprehending radiology reports. We created four groups based on two factors—the extent of explanations for the prediction (High vs. Low Transparency) and the model performance (Good vs. Weak AI Model)—and randomly assigned participants to one of the four conditions. Participants were instructed to classify a radiology report a s describing a normal or abnormal finding, followed by completing a post-study survey to indicate their perceptions of the AI tool. We found that reveal...
Source: Journal of Medical Systems - Category: Information Technology Source Type: research